{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Machine learning pipelines using battery data\n",
    "Authors: Dr. Paul Gasper, Andrew Schiek, Dr. Kandler Smith (NREL)\n",
    "\n",
    "This notebook demonstrates how to use machine-learning to develop predictive models for battery health diagnosis, using electrochemical impedance spectroscopy (EIS) to predicy discharge capacity. This challenge is used as an example to demonstrate fundamentals of machine-learning, such as data standardization, train/test splits, feature engineering, and hyperparameter optimization. This tutorial also serves as a summary of how to use the machine-learning library sklearn, especially the functionality of Pipelines, which are extremely useful tools for automating the data processing and model optimization process in a way that is repeatable and understandable.\n",
    "\n",
    "This tutorial uses open-source data from Zhang et al, *Nature Communications* (2020) 11:1706 ([paper](https://www.nature.com/articles/s41467-020-15235-7.pdf), [github](https://github.com/YunweiZhang/ML-identify-battery-degradation))."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from eisplot import plot_eis\n",
    "from plotyy import plotyy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load data\n",
    "eis_data = pd.read_csv(\"data/data_eis_zhang2020.csv\")\n",
    "freq = np.loadtxt(\"data/freq_eis_zhang2020.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Processing impedance data\n",
    "Investigate the dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "...         ...    ...        ...       ...            ...              ...   \n",
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       "[1343 rows x 244 columns]"
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     },
     "execution_count": 3,
     "metadata": {},
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   "source": [
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  {
   "cell_type": "code",
   "execution_count": 4,
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       "      <th>ZPhase_0.032Hz</th>\n",
       "      <th>ZPhase_0.025Hz</th>\n",
       "      <th>ZPhase_0.02Hz</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1343.000000</td>\n",
       "      <td>1343.000000</td>\n",
       "      <td>1343.000000</td>\n",
       "      <td>1343.000000</td>\n",
       "      <td>1343.000000</td>\n",
       "      <td>1343.000000</td>\n",
       "      <td>1343.000000</td>\n",
       "      <td>1343.000000</td>\n",
       "      <td>1343.000000</td>\n",
       "      <td>1343.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1343.000000</td>\n",
       "      <td>1343.000000</td>\n",
       "      <td>1343.000000</td>\n",
       "      <td>1343.000000</td>\n",
       "      <td>1343.000000</td>\n",
       "      <td>1343.000000</td>\n",
       "      <td>1343.000000</td>\n",
       "      <td>1343.000000</td>\n",
       "      <td>1343.000000</td>\n",
       "      <td>1343.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.940432</td>\n",
       "      <td>106.400596</td>\n",
       "      <td>26.496332</td>\n",
       "      <td>0.729017</td>\n",
       "      <td>0.340489</td>\n",
       "      <td>0.347315</td>\n",
       "      <td>0.354893</td>\n",
       "      <td>0.363001</td>\n",
       "      <td>0.371858</td>\n",
       "      <td>0.381273</td>\n",
       "      <td>...</td>\n",
       "      <td>-4.884943</td>\n",
       "      <td>-5.385130</td>\n",
       "      <td>-6.023799</td>\n",
       "      <td>-6.798514</td>\n",
       "      <td>-7.732075</td>\n",
       "      <td>-8.706260</td>\n",
       "      <td>-9.836724</td>\n",
       "      <td>-11.098813</td>\n",
       "      <td>-12.411253</td>\n",
       "      <td>-13.771376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.157238</td>\n",
       "      <td>70.356673</td>\n",
       "      <td>5.287921</td>\n",
       "      <td>0.144712</td>\n",
       "      <td>0.062096</td>\n",
       "      <td>0.061680</td>\n",
       "      <td>0.060970</td>\n",
       "      <td>0.060001</td>\n",
       "      <td>0.058931</td>\n",
       "      <td>0.057523</td>\n",
       "      <td>...</td>\n",
       "      <td>0.427160</td>\n",
       "      <td>0.438623</td>\n",
       "      <td>0.516722</td>\n",
       "      <td>0.630544</td>\n",
       "      <td>0.754696</td>\n",
       "      <td>0.846736</td>\n",
       "      <td>0.937842</td>\n",
       "      <td>1.012982</td>\n",
       "      <td>1.055221</td>\n",
       "      <td>1.082193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.862190</td>\n",
       "      <td>0.184414</td>\n",
       "      <td>0.265460</td>\n",
       "      <td>0.274690</td>\n",
       "      <td>0.280460</td>\n",
       "      <td>0.291470</td>\n",
       "      <td>0.301120</td>\n",
       "      <td>0.311110</td>\n",
       "      <td>...</td>\n",
       "      <td>-7.423670</td>\n",
       "      <td>-7.850090</td>\n",
       "      <td>-8.638000</td>\n",
       "      <td>-9.522380</td>\n",
       "      <td>-10.686650</td>\n",
       "      <td>-11.858560</td>\n",
       "      <td>-13.319870</td>\n",
       "      <td>-15.033650</td>\n",
       "      <td>-16.548570</td>\n",
       "      <td>-18.337440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>44.500000</td>\n",
       "      <td>25.396067</td>\n",
       "      <td>0.711715</td>\n",
       "      <td>0.291670</td>\n",
       "      <td>0.299125</td>\n",
       "      <td>0.307680</td>\n",
       "      <td>0.316955</td>\n",
       "      <td>0.326895</td>\n",
       "      <td>0.337915</td>\n",
       "      <td>...</td>\n",
       "      <td>-5.005195</td>\n",
       "      <td>-5.552185</td>\n",
       "      <td>-6.299210</td>\n",
       "      <td>-7.180840</td>\n",
       "      <td>-8.238390</td>\n",
       "      <td>-9.339950</td>\n",
       "      <td>-10.547290</td>\n",
       "      <td>-11.848600</td>\n",
       "      <td>-13.194300</td>\n",
       "      <td>-14.593005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>27.610685</td>\n",
       "      <td>0.762050</td>\n",
       "      <td>0.309170</td>\n",
       "      <td>0.316830</td>\n",
       "      <td>0.325370</td>\n",
       "      <td>0.334600</td>\n",
       "      <td>0.344580</td>\n",
       "      <td>0.355200</td>\n",
       "      <td>...</td>\n",
       "      <td>-4.805650</td>\n",
       "      <td>-5.350250</td>\n",
       "      <td>-5.977890</td>\n",
       "      <td>-6.665620</td>\n",
       "      <td>-7.514200</td>\n",
       "      <td>-8.445980</td>\n",
       "      <td>-9.580070</td>\n",
       "      <td>-10.803000</td>\n",
       "      <td>-12.163530</td>\n",
       "      <td>-13.601140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>159.500000</td>\n",
       "      <td>29.622224</td>\n",
       "      <td>0.811950</td>\n",
       "      <td>0.392680</td>\n",
       "      <td>0.398855</td>\n",
       "      <td>0.405635</td>\n",
       "      <td>0.412715</td>\n",
       "      <td>0.419630</td>\n",
       "      <td>0.427485</td>\n",
       "      <td>...</td>\n",
       "      <td>-4.664185</td>\n",
       "      <td>-5.139475</td>\n",
       "      <td>-5.667920</td>\n",
       "      <td>-6.323970</td>\n",
       "      <td>-7.136675</td>\n",
       "      <td>-8.023815</td>\n",
       "      <td>-9.056790</td>\n",
       "      <td>-10.237160</td>\n",
       "      <td>-11.492735</td>\n",
       "      <td>-12.840890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>8.000000</td>\n",
       "      <td>275.000000</td>\n",
       "      <td>37.210831</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.467900</td>\n",
       "      <td>0.475960</td>\n",
       "      <td>0.483980</td>\n",
       "      <td>0.489170</td>\n",
       "      <td>0.498700</td>\n",
       "      <td>0.506240</td>\n",
       "      <td>...</td>\n",
       "      <td>-4.156830</td>\n",
       "      <td>-4.554650</td>\n",
       "      <td>-5.063680</td>\n",
       "      <td>-5.633700</td>\n",
       "      <td>-6.441180</td>\n",
       "      <td>-7.338270</td>\n",
       "      <td>-8.252760</td>\n",
       "      <td>-9.439000</td>\n",
       "      <td>-10.555270</td>\n",
       "      <td>-11.995620</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 244 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         seriesIdx        cycle            Q            q  ZReal_2e+04Hz  \\\n",
       "count  1343.000000  1343.000000  1343.000000  1343.000000    1343.000000   \n",
       "mean      3.940432   106.400596    26.496332     0.729017       0.340489   \n",
       "std       2.157238    70.356673     5.287921     0.144712       0.062096   \n",
       "min       1.000000     1.000000     6.862190     0.184414       0.265460   \n",
       "25%       2.000000    44.500000    25.396067     0.711715       0.291670   \n",
       "50%       4.000000   100.000000    27.610685     0.762050       0.309170   \n",
       "75%       6.000000   159.500000    29.622224     0.811950       0.392680   \n",
       "max       8.000000   275.000000    37.210831     1.000000       0.467900   \n",
       "\n",
       "       ZReal_1.6e+04Hz  ZReal_1.3e+04Hz  ZReal_9.9e+03Hz  ZReal_7.8e+03Hz  \\\n",
       "count      1343.000000      1343.000000      1343.000000      1343.000000   \n",
       "mean          0.347315         0.354893         0.363001         0.371858   \n",
       "std           0.061680         0.060970         0.060001         0.058931   \n",
       "min           0.274690         0.280460         0.291470         0.301120   \n",
       "25%           0.299125         0.307680         0.316955         0.326895   \n",
       "50%           0.316830         0.325370         0.334600         0.344580   \n",
       "75%           0.398855         0.405635         0.412715         0.419630   \n",
       "max           0.475960         0.483980         0.489170         0.498700   \n",
       "\n",
       "       ZReal_6.2e+03Hz  ...  ZPhase_0.16Hz  ZPhase_0.13Hz  ZPhase_0.1Hz  \\\n",
       "count      1343.000000  ...    1343.000000    1343.000000   1343.000000   \n",
       "mean          0.381273  ...      -4.884943      -5.385130     -6.023799   \n",
       "std           0.057523  ...       0.427160       0.438623      0.516722   \n",
       "min           0.311110  ...      -7.423670      -7.850090     -8.638000   \n",
       "25%           0.337915  ...      -5.005195      -5.552185     -6.299210   \n",
       "50%           0.355200  ...      -4.805650      -5.350250     -5.977890   \n",
       "75%           0.427485  ...      -4.664185      -5.139475     -5.667920   \n",
       "max           0.506240  ...      -4.156830      -4.554650     -5.063680   \n",
       "\n",
       "       ZPhase_0.082Hz  ZPhase_0.064Hz  ZPhase_0.051Hz  ZPhase_0.04Hz  \\\n",
       "count     1343.000000     1343.000000     1343.000000    1343.000000   \n",
       "mean        -6.798514       -7.732075       -8.706260      -9.836724   \n",
       "std          0.630544        0.754696        0.846736       0.937842   \n",
       "min         -9.522380      -10.686650      -11.858560     -13.319870   \n",
       "25%         -7.180840       -8.238390       -9.339950     -10.547290   \n",
       "50%         -6.665620       -7.514200       -8.445980      -9.580070   \n",
       "75%         -6.323970       -7.136675       -8.023815      -9.056790   \n",
       "max         -5.633700       -6.441180       -7.338270      -8.252760   \n",
       "\n",
       "       ZPhase_0.032Hz  ZPhase_0.025Hz  ZPhase_0.02Hz  \n",
       "count     1343.000000     1343.000000    1343.000000  \n",
       "mean       -11.098813      -12.411253     -13.771376  \n",
       "std          1.012982        1.055221       1.082193  \n",
       "min        -15.033650      -16.548570     -18.337440  \n",
       "25%        -11.848600      -13.194300     -14.593005  \n",
       "50%        -10.803000      -12.163530     -13.601140  \n",
       "75%        -10.237160      -11.492735     -12.840890  \n",
       "max         -9.439000      -10.555270     -11.995620  \n",
       "\n",
       "[8 rows x 244 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eis_data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are several variables in this data frame:\n",
    "- seriesIdx: index denoting which cell (1-8) was the data is from\n",
    "- cycle: cycle number count\n",
    "- Q: capacity of the cell in mAh\n",
    "- q: relative capacity of each cell (capacity divided by initial capacity, calculated for each cell)\n",
    "- ZReal_xHz: real component of the impedance at x Hz frequency\n",
    "- ZImag_xHz: imaginary component of the impedance at x Hz frequency\n",
    "- Z_xHz: magnitude of the impedance at x Hz frequency\n",
    "- ZPhase_xHz: phase angle of the impedance at x Hz frequency\n",
    "\n",
    "So, for training a model, the target variable is Q, while the features are all the components of the measured impedance at each frequency. The frequency vector for these measurements can be created using `np.logspace` or loaded from file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((60,), 20004.453, 0.01999)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Frequency vector\n",
    "freq.shape, np.max(freq), np.min(freq)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1343,), (1343, 240))"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = eis_data['Q']\n",
    "is_Z_col = eis_data.columns.str.contains('Z')\n",
    "x = eis_data[eis_data.columns[is_Z_col]]\n",
    "y.shape, x.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data is now extracted, and the shapes look correct (same number of rows in x and y, 240 columns in x corresponds to 4 components of the impedance for 60 frequencies).\n",
    "\n",
    "To plot the impedance data, we need to reform these features into arrays of complex numbers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60,)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "is_ZReal_col = x.columns.str.contains('ZReal')\n",
    "is_ZImag_col = x.columns.str.contains('ZImag')\n",
    "eis = x.apply(lambda d: d[is_ZReal_col].values + 1j*d[is_ZImag_col].values, axis=1)\n",
    "eis[0].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing the data\n",
    "Visualizing the raw data can help us understand the complexity of the problem. \n",
    "We should plot both the capacity (our target variable) as well as all of our impedance data (our features). The task of plotting both features and target variables can be very challenging, but is very important for getting an intuitive understanding of your data set.\n",
    "\n",
    "### Visualizing capacity\n",
    "Plot the capacity of each cell versus cycle count."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1c9f8ac99c8>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "series = np.unique(eis_data.seriesIdx)\n",
    "# Customize the colors to match with scatter plots later in the notebook\n",
    "cmap = plt.get_cmap('tab10', series.size)\n",
    "for this_series in series:\n",
    "    mask_series = eis_data.seriesIdx == this_series\n",
    "    plt.plot(eis_data.cycle[mask_series], eis_data.Q[mask_series], '-',\n",
    "        color=cmap((this_series-1)/series.size), linewidth=2, label='Series %d' % (this_series))\n",
    "plt.ylim([0, 40])\n",
    "plt.xlim([0, 300])\n",
    "plt.xlabel('Cycle count')\n",
    "plt.ylabel('Discharge capacity (mAh)')\n",
    "\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There's wide variation in the magnitude of the initial drop, and then all cells stabilize for between 20 cycles (before testing is cutoff, series 4 and 8) to about 200 cycles (series 1 and 5). Some cells display an extremely sudden drop of capacity at 100 cycles (series 7), while others have just a slight increase in the degradation rate at 100 cycles (series 1 and 6), and other cells have a mix of behaivors (series 1: change of slope at 100 cycles, sudden drop around 200 cycles)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizing impedance data\n",
    "Plotting just one curve gives us an idea of the typical 'shape' of an impedance measurement, as well as the relationships between the components of the impedance. |Z| and Z_Real are both 'magnitude' features, which are always increasing as frequency decreases, while Z_Imaginary and phase of Z are both 'phase' features, which vary non-monotonically versus frequency."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_eis(freq, eis[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting all of the data for one of the cells can help us understand how capacity and impedance vary throughout the lifetime of a cell."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "this_series = 1\n",
    "mask_series = eis_data.seriesIdx == this_series\n",
    "\n",
    "plt.scatter(eis_data.cycle[mask_series], eis_data.Q[mask_series],\n",
    "            c=eis_data.cycle[mask_series], cmap='viridis')\n",
    "plt.xlabel('Cycle count')\n",
    "plt.ylabel('Discharge capacity (mAh)')\n",
    "plt.ylim([0, 40])\n",
    "plt.xlim([0, 300])\n",
    "plt.title('Series %d' % (this_series))\n",
    "\n",
    "plot_eis(freq, eis[mask_series], cmap='viridis', title='Series %d' % (this_series))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Impedance seems to be increasing throughout the lifetime of the cell. Also, the locations of peaks and valleys in the phase of Z and Z_Imaginary seem to vary. \n",
    "\n",
    "Let's plot the data from one more cell."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "this_series = 2\n",
    "mask_series = eis_data.seriesIdx == this_series\n",
    "\n",
    "plt.scatter(eis_data.cycle[mask_series], eis_data.Q[mask_series],\n",
    "            c=eis_data.cycle[mask_series], cmap='viridis')\n",
    "plt.xlabel('Cycle count')\n",
    "plt.ylabel('Discharge capacity (mAh)')\n",
    "plt.ylim([0, 40])\n",
    "plt.xlim([0, 300])\n",
    "plt.title('Series %d' % (this_series))\n",
    "\n",
    "plot_eis(freq, eis[mask_series], cmap='viridis', title='Series %d' % (this_series))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This battery capacity rapidly fades at beginning-of-life before stabilizing at around 26 mAh. Throughout this the impedance decreases slightly.\n",
    "\n",
    "The wide variance between trends implies that it should be challenging to predict capacity from impedance."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training a model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can train a simple linear model predicting capacity with very few lines of code using sklearn. Best practice is to normalize the input features before training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import regression model class\n",
    "from sklearn.linear_model import LinearRegression\n",
    "# Import data normalization tool\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "# Import standard statistical metrics for evaluating model performance\n",
    "from sklearn.metrics import mean_absolute_error, r2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 0.36, R^2: 0.99\n"
     ]
    }
   ],
   "source": [
    "# Normalize the training data\n",
    "scaler = StandardScaler()\n",
    "x_ = scaler.fit_transform(x)\n",
    "# Instantiate the linear model class object\n",
    "regressor = LinearRegression()\n",
    "# Fit the linear model\n",
    "regressor.fit(x_, y)\n",
    "# Make the predictions using the fit model\n",
    "y_pred = regressor.predict(x_)\n",
    "print(\"MAE: %0.2f, R^2: %0.2f\" % (mean_absolute_error(y, y_pred), r2_score(y, y_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotyy(y, y_pred, color=eis_data.seriesIdx)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "While this looks impressive, it's seems suspiciously good. Should we really be able to predict the capacity of a battery with such high accuracy after making so little effort?\n",
    "\n",
    "One common method to test our model performance is to hold out data from training to test a model on, i.e., the test set. A common way to test a model is to randomly hold out some fraction of the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((940, 240), (403, 240))"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_test, y_train, y_test = train_test_split(x_, y,\n",
    "    test_size=0.3, random_state=42)\n",
    "\n",
    "x_train.shape, x_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 0.46, R^2: 0.99\n"
     ]
    },
    {
     "data": {
      "image/png": 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5yljs689WOchAliPUnL5BPEJSaloTXYGJVUxz3BhjnFrs1YsXsujDd9leWkE/Tx/W9loLhA5tEy4PE9of4qzANEacdhZVnbND5QsIhVeDJrBXTkVYVWM/APF46TVy9LEHdjAf5j8KO5dDzgg49VZo1+34L9TUYEnSmBjs27ePp/7yMMF1KwmnZqCJSXzVq5CghPCGIex1EuS2P+SjO4WBXx+H+A8XMX3Jp7JPOjvvI8XknnQp21a+hcfrxeMV/ElJTPnpffj8dRRLo9m1Ev5xAYQrnVfeF7DoH3Dzh9BpcEN+BW1WLEOled3b7mMiIknAbCDRPc8rqnqfO3DvizhjU24BrlTV/cd6fGOaiqry7DPPEFm1CC/gPbAHgNEFSmhQCuXJyra0Unb9fjdlW0u5cdK9JPtr3jaL+OkqfUmQcm6+cBjTzx9BMPANdny1Fl9CAjkDBuLxHEdbyX/dBZUlhz9XJct3fgw3vn0CV22qxFKS3CAirwBPqurqYzh2ADhXVUtExA/MEZF3gSnAR6r6WxG5G6eG/KfHHLkxTWTXrl1UrF2Ol5qTPHkUTl3bkfJwiJWz13NwTxE3TbqXUX3OqvM4KSqclNOemyYOB8CfmETPk0Yef2CqkD+/7nV5c4//uKaGWIYWOQlYBzwuIvNE5BYROerAduqo+hPnd18KXIYzaAbuz8uPOWpjGsBXpRU8tb2QtwsOUBGuf+yWkpISvAcKa82CJwgeFT5euYHtew5y47l315sgwZlq9PaOWQ03oo8I+JLrXuevZ7k5ZrH0uCkGHgMeE5EJOE2C/uiWLh9U1Q317evOIbEIp23lX1V1voh0VtWd7rF3ikidjcJE5BbgFoDc3NxjvCxj6hdR5ftr8nhrzwEAfCL4RHh1VD+GpNVMLqWlpbz00kskav1JdOzYXgzs1p/hfc+sc72qIiIkIvgSGrj74ajrYPHTEKo4vMyXBCff0LDnacOO+idNRLwicqmIvAY8DPwB6AO8BbwTbV9VDavqSKA7MFZEhsUamKrOUNUxqjomOzs71t2MOarXdu/nX3sOUhFRKiJKSTjCgVCY65dvQrVms5yXXnudT8tyCImXUEo64aQUFKgIhnh18UrKK4N8MWY/FWPKUQ3UcTY91NXQ5/cwcFyXhr2Y834FvSc4iTExw/nZ5xyY+MuGPU8bFsszyfXAx8DvVPWLastfcUuWR6WqB0TkE+BCYLeI5LilyBxsxHPTxJ7dsZeySO2S4b5gmDWlFQxJSyYYUe7fsJ0lGwJEVCgfMMLtmy1UlpUy69lnyCvcx+CunahIirB48H62lczgklXfxR9JQNybc4+/DDQVwcPYS3qT3SO9YS/GnwTXvAx7N0LhesgeAB36NOw52rhYkuT1qjqn+gIROUNVP1fVO+rbSUSygaCbIJOBSTiD974J3AD81v35xnFHb8xxqDyitFjFI1AZUVSVH61azvz129BAEuclrHend/UQCAR4/uVX2Fa4j2+MG0nHQRkEEpwZAPak5fH0yb/i9PzrOSt7NUkdNtGx3SV0yLiM3KEdSWuf2HgX1bGv8zINLpYk+Wec7ojVPVLHsiPlAE+7zyU9wEuq+raIzAVeEpGbgTxg+jHGbMwJmdq5PWtKyimP1EyWFeEI0z9/Bf/eJ/CEi/FomOEZZ+OpaA/gJMjnn2dbfj7TpkxhWLqfFVl5hw8Q8VF2cAw7Mw7Qof9HVBZ3Jlx0JkO+1rUpL880sHqTpIicBpwOZIvID6utyqBqBNEoVHU5h8eirL58L04/cGPi4tquHXl9935Wl1ZQWlWrrYpWbiNxz8OIOzGoCnTQCB63Wru8vJzi4mKmTZvGwEFDYMdGhm7NYEX/g/g9cGZGBT2TN1NWFKRg+RSKNp/JsAkJcbpK01CilSQTgDR3m+oPUoqAaY0ZlDGNKdHj4fWT+/NUfiG/3LCdMIAIySXvgR7uKtjvYD8yKzMJBoP4fD4yMzO57bbb8Pl8aETxlhUjKqRUerijZxmd/UowvIHS0vbs2vB1fAke+o/tHLfrNA2j3iSpqp8Cn4rIU6q6tQljMqZxBUrwAjsCNUfi8QV3Ie5cdxIR2lW2IxAI8OLzL9KpUycmT56Mz+cjQoTEvTvxhEOEvRDwR1hR7qWzP4TfGyIj90sKl9/ASef0pEvvdnG4QNOQot1u/0lV7wT+IiK1nnTblLKmxdm3GV7/DuR/CcDaEY8QTh/sNMoGKhP64qtcjwAqyqKURWyZsYXS/FJOPfXUQ4ep8FSwLmsVI/dnsqp3EREPhKv9hng8MOUnw8jultOUV2diJCKZwOPAMJwOLt9U1Xq7KEW73X7W/fn7BovOmDiJVJYx462HmZnzLUq7305wRZAD69NghILPSZKihxtkhyvCbH14K2Ubyxhy8xCGdB1yaF1xQjFrs0vwhoXl/YrwCwxPPjy8QXJKV7K6NnB7SNOQHgbeU9VpIpIARJ27N9rt9iL37UKgXNXpcuDWVjdiWwZjGljJHt57+z5Ghz/l/PwXkYIUtgW68FToAt4rnEQkKwl8HpLKFzmlSFXy/pJH2YYyenynB94xXkJbQ/jUR0hCfJX5FWGvsrz/QbzA2elhuiYo4MXjSWDQwN/YXDXNlNulegJwI4CqVgKV0faJpQnQRzhtHKv6YScDH+DUfBvTvH31LvryTZwfqcDrNvkRyujtKWSMfz1PrFrPm93H0TFxP+vC5QT9ICJkXZBFh7M70O6UdqAQkhARibCswzL2JO9xDy6EUSp9XcnIaEdKSl965n6LtLSB8bteA5AlIgurfZ6hqjPc931wpqJ5UkRG4HSb/r6qltZ3sFiSZFK1gSpwR/WJWjw1plmoLIVXvomEyuv8jx7MCHPS0Dn0lQX4JcSLu718tChA2qgM0k9yG3QohCvb8156AeHMxc4kNjUIc/bv5d5zZ9Il1W6xm4lCVR1TzzofThvv77ljSTyMMxLZL+o7WCzDkZSKyKGG4yIyGig/hoCNiY9Nn4LU3aS3hCRmJk5mzZbT8HvDlAcizP7Nbrb+dRuRwggoaDiBSDiVsm03c3DPZMq23I6Ga7d79Hl8zNs5r7GvxjSMfCBfVavGmHuFo3SMiaUkeSfwsojscD/nAF8/3giNaTKRoDMA7REU2EQuBYW9GDHyfSoqwtzzs51sXF/J1KlTaR9qz9zCRCoqOxMqHgbqjBaulVkE955FQqeaUz75xEe6v4H7ZJtGoaq7RGSbiAxU1a9wOrZEHSc3lqHSvhSRQcBAnDFH16pq7anejGlOKorgP7+GcO2ReYL4WMYwQPB6C/jZz3ayZk2AqVOnMmTIUErKEigLDCd8xI1WRP0Ei0bWSpIeEcZ3tynoW5DvATPdmu1NwE3RNo51jpuBwBCcebdHiQiq+swJhWlMAwoECtiW/wxFRctI93Yl9/3nSSwtqrWdAtvpzEZyAeHTTytYszrAPT/vgt83iEgEPCh1D4EB6b4QXlG87rF8Ivz9vEdJ9FqDj5ZCVZcC9T2zrCWWOW7uA87GSZLvABcBcwBLkqZZKC3dyJcLpxAOlwNh9iuQFaRfKXWMJg576EjVmuHDxvDojBX07u1l/fqNFOzuS0oEMqWcfZpM9cf2fk8l53f/gnO6lrMx4EGADZEunJR9UpNcp4mPWCpupuHct+9S1ZuAEVg7SdOMrFh5B+FwCaphioqyKCzMpd2BusuCChwICLNmPc/OnTvZunUkqal9iUQ89O65mKyOW/FIiPP9a0gmRBKVeAiSQIBTvGuZ0OE/VCqkeODTEj9bwh2b9mJNk4vldrtcVSMiEnIbYhbgtDUyJu4qKw9SWrqWQCCZFcvPI1CZgqBcGP6qVikSoCSg3DdzHhvyQ4weORzt0pmv1oxns7+U5JRiMg4WcztPUiap/NA7g39UnEt+cn++7XmN4Z5NvLwpjV9ndQAgyZvEHcOnNu0FmyYXS5Jc6PZ1fAyn4WUJsKAxgzImVnM+n4gqrFl9FuXl6VTdHIXrGM2vOKBcNLOML/PDvDAtmb2DO7FbQUWoDKVRWZTGQe3EI+FcOm5fTbAyRHZOBXf6HiGJSsrFw6cZHUjwJOARD2O7jOWqQVc18RWbphZL7fZt7tu/i8h7QIY7VqQxcbVnz2Iikf1UViZTVNyBtaHOrA13JqQeir3f4B7fTDpKMQAllU6CnOcmyIuGZPAQHVE54omTxwvBSioDEXqkFDElfR5eIuBPIWHoFVwx8nLOKN/DiOwRDMuKecom04LFVLstIlOA8TiPdOYAliRN3K1f/y8AwmE/c4J92BLueKgE+Ub4dOaFB/NB4k9IlQB+D2SlCC9MS2baED8H8CH11GEnpKYx7ef/TY/OycjKVyBYBoMuxttjLOdZn+wm560oI33NwqNv2Ehiqd3+P5wpYWe5i24VkUmq+t1GjcyYo4hEUgEo1oQaCRIghI8CMnm0bCI3ev5Fh2Thta8nHxp4oh3FpFLGQWqO9+j1ehk15hRyh7k11uf8rGkuxjRbsdRunwVcoKpPquqTwNdwmgQZE1cDBzodv7YU5RLx1P6vHAgEeeCFRUx8LkA4ojVH5lG4gvfxU4nX3dfv95OWlsaECTFNAmraiFhut78CcoGq0cl7YLfbJo7Wrl3LZ599RnFxMYmJZ5KRsbfWNpFAGQUv30fljo1MmDYer2fpoXWqEFLBk7OX72SVsTjzXPbv30+vXr046aSTSEy0Fm7msFiSZEdgjYhU1WifAswVkTfBRig3TWv+/Pn8+9//JhgMAsq5bGNcwRJe1svJoxOKx02Q9xPY8RUXT/k6XYf0AJaiOA/VgxEPT288mamjxtPhsvuYVEcp1JgqsSTJXzZ6FMZEsXXrVj7//HMOHDhAYWEhkYgzD81oVjCOpSRImOcTfs3Uyt9QoOns/fD/COxYy6Qp1zB6WC+6dlrBkux0QCj/KoUlX/WkQhNpf9kvnbkWjIkiliZAnzZFIMbUZdmyZbz99ttuybGmM/iSBJzZDbvJPu5NfIm5OoI9k06haFgXBg3qzdChH9Gh3U72+Zxb6PApYTqm76N7l+/j8Rx1ZmRjjl5xIyLjRORLESkRkUoRCYtI7ZEDjGlg4XCYd999t84ECZDC4TlpigPKijnvc7KupEe7BAb370dmu+1ktttdoyjg9SvZw0s4+RIbtcfEJpZ7jb8AVwPrcaZu+Ja7LCoR6SEiH4vIGhFZJSLfd5ffLyLbRWSp+/raiVyAab327dt36Na6OncWBraRQ4TDPWl+8Z8Kuu94j1uCT5GyZTU5niV4vEeOJA4ej5d9+z5v5OhNaxFTY3JV3SAiXlUN48wN8UUMu4WAu1R1sYikA4tEpGogvj+qqs3CaKJKTk4mHK6d5AJ4SdMAH8tpdAhs57KZB5mXH2bm1GRO7prAezu6kRgso7uviICCHtH+W8SLz5fRRFdhWrpYkmSZOzjlUhH5f8BOIPVoO6nqTndbVLVYRNYA3U4kWNO2pKWl0bt3bzZt2lSjRNmBYv6LZzkQ8HLpzIPMzw8za2oy04f6KQl5OaVjPhd2XUeoCOZp+zr61QjZ2ZOa8lJMCxbL7fZ17na3A6U47SSPaegTEekFjAKq5pW4XUSWi8g/RKR9PfvcIiILRWThnj176trEtAFTp06lW06O07hRnXRXKQkkU8nOPftZsftwggRI8wXpklyC3xMhORBh6NpiPGFnP48nGb+/PaNGPonXa3PZmdjEUpIsBCpVtQJ44Fjn3RaRNOCfwJ2qWiQifwMexGmy9iDwB+CbR+7nTgE5A2DMmDH1DRRtWqm8vWW8v2oXItBv6Ei2bcsDQMIRggiLvMMY220pm7+fRseU+v/Wd9pbSeaCgxR+81GSk3Jp1+5kPJ5YB+Q3ppHn3RYRP06CnKmqrwKo6u5q6x8D3j7GmE0rFYlEWL16NW99PJf8PQfZq6ls1GxG6Rb67N6Cr+QAFcEQT3/2JWt6d+O58z10SD68vyrUHn9CSOh1Ll1zpjXlpZhWpNHm3Rano+wTwBpVfaja8hz3eSXAFcDKY4zZtEKqygsvvMDGTZsIh0JkeyBLSxjAbpLz1uErLyZQGeSJzxaQt/cA4/v15MmNo5nYZSN90vc50zJUpNAxsRyv14NoGDx+8CfB+b+O9+WZFiyWJFkqIier6mI4pnm3z8B5nrlCRJa6y+4BrhaRkTi321uAW48xZtMKbdq0ic2bNxMOhQ4tEwFPZQV+N0E+7ibIa8aN4qQeOQT8Pj7YNYCK7V6SvUGSPCH8SUlMP7cLSaV50H0MnHY7ZPaI45WZ5kZEtgDFQBgIqWrUScEabd5tVZ1D7XmYwJlMzJgaNmzYUGejcU9lgJBSI0GO6JEDQGWlEoo4FTbl4UTKw4kk+JLxTZ8Bfn+Txm9anHNUtTCWDW3ebdMsJCcn4/F4ajUejySl4BMY2rUzZ/bvfShBhvGgEUWQQ4Pn+hITGX/VDfgsQZoGFGtj8iD27NA0koqKCvr27cvs2bNrJMlAIMD+/fvp0S6LCYMFrzrrIkDQ4+eNzpMZWbKKMYn7Se/QkbGXT6fv6LFxugrTgijwgYgo8KjbkqZe1hbCxE1paSmvvfYamzdvRlXx+Q7/dwwEKpg583kKCwv50Y+/SZd+G8lb1hV/OMi25B7MbX8qRf4M5mR0ZcavLozjVZhmKEtEqs/3MOOIRHiGqu4QkU7AhyKyVlVn13cwS5ImLlSVZ555hj179hwqPVZWVgJOCXLmzOfJz8/nW98ex5lnvo+Kh7UdB/PyusPDl/q9wsUn5cQlftOsFUarjFHVHe7PAhF5DRgL1JskYxkFSETkWhH5pfs5V0TsnsackPz8/BpjQ1ZxEuRM8vPzmTZtGj1zz0RE8UqIUZ3WkproJcErpCZ46ZOVxs8nD4nTFZiWSERS3bEkEJFU4HyO8igxlpLk/+E8BjoX+BVO1fk/cUYoN+aYqSoffPBBnYNXfPHFF4cS5JAhQwiHgwQqUklKLmFw9z78pf/JbCwoYUDndMb3y8LjsdkLzTHpDLzmznfkA55X1fei7RBLkjxVVU8WkSUAqrrfHfDCmOOyfv161u/Yx7JgD/ZFkrkgYR1VuW7ChAn06dOHnj17AqDqweurxONJpmfutxjVoRPnDOwUx+hNS6aqm4ARx7JPLANcBN3+2gogItk4JUtjjsu781bySulA1oY7sVvbsb4shTfffJPS0lK8Xu+hBAkRMtvvJinJR/9+d9Ohwxlxjdu0TbEkyT8DrwGdROQ3wBzgvxs1KtOqvZrnJ4T30KRdr896iqVLl5G/fcehwX5UYS/JzJdTOXP8l3Tvfm28wzZtVCyNyWeKyCJgIk5j8stVdU2jR2ZaJVUlr8T521x9VsOsS3/Cll7DKQ6VElYP27QdJZpMxwMJeL1JcY7atGVHTZIi0gEoAGZVW+a3XjcmVqrKypUrmTdvHuXlFfg9fagor0qQa8m69CekDhrPzgjspN2h/bwC5wyy548mvmKpuFmMM9DufpySZCawU0QKgG+r6qLGC8+0Bh988AFffvklIXfwir6Swuqgj0ig5FCCPJLfK2Qk+bnr/AFNHa4xNcSSJN8DXlPV9wFE5HzgQuAlnOZBpzZeeKalKy4uZv78+TUajI/ybqUkYwBy48Pgrd3P2iNw7biefO/c/nRItYYUbZ14O5PU/ofHsMe/GvT8sVTcjKlKkACq+gEwQVXncQwjlJu2aceOHYfeBwIBnnvuOf715htMTNjAuclb8XtrtnP0e4WTc9tz3yVDLUGaZiGWJLlPRH4qIj3d10+A/W6zIGsKZKJKT08nEonU6EkzcOBAAHr5DnLveT3JSPaRkuAlwedhbO8OPHZ91OH9jGlSsdxufwO4D3jd/TzHXeYFrmycsExLU1BQwJw5c9i9ezddunThzDPPJCsri5ycHCKRSI2uhkOGOF0JRYQpo3P5xpmD2bq3lIxkP53SrSbbNC9Rk6RbWvyTqtbXSG1Dw4dkWpq8vDyeffZZQqEQqsru3btZsWIFI0eOZPDgwbz11lu1EqTH46F3796kp6cD0K9TejwvwZh6RU2SqhoWkWwRSVDVyqYKyrQs77zzTq1RxSORCIsXL2bx4sWMHDmS/v37H0qQAL169WLaNJucyzR/sdxubwE+F5E3cebdBqD65F6mbYhEImzevJnS0lJyc3PJzMwkEomwa9euWtsGAgE2bNjA0KFDq3UzdPh8PgYMGEBSkt1am+YvliS5w315ALsnaqP27t3L008/TUVFBeAkzNGjR3PBBReQkJBwaCxIcBLk888740F27dqV9u3b1zhWKBQiLy+PcePGNek1GHM8YumW+EBTBGKaL1Xl+eefp6ioqMbyxYsXk5uby9ixY5k3bx6hUOhQgty2bRvTpk2rlSABvF4vWVlZTRW+MScklkF3s0XkdyLyjoj8p+rVFMGZ5mHPnj21EiRAMBjkyy+/ZMKECXTp0qVWgqz+DLI6r9fL6NGjGztsYxpELO0kZwJrgd7AAzjPKL9sxJhMM3NkpYwqVKiXvFAmG/ILmDlzJrt372b9+vW1arGPlJWVxfXXX0+7du3qXG9McxPLM8mOqvqEiHxfVT8FPhWRTxs7MNM8bNu2rVbttQgkEaart4h3SgcxbPMe+niCDBs2jK5du9KhQ4dD23o8HrxeL6mpqVx99dV07tw5HpdhzHGLJUlW/XbsFJHJOJU43RsvJNNc7N69m2eeeaZWSbKKhwj9Qlt55aUXueKs0fTPzTmUIH0+H+PGjSMzM5PMzEz69OmDxxPLjYsxzUssSfLXItIOuAt4BMgAfnC0nUSkB/AM0AWn++IMVX3YHXrtRaAXzq37laq6/7iiN41q9uzZh0buqUuwMsCcF5+mPD+PLaNG0r/aOo/HQ05ODkOHDm38QI1pRLHUbr/tvj0InHMMxw4Bd6nqYnd2skUi8iFwI/CRqv5WRO4G7gZ+emxhm6aQl5eHqta5rqov9q787XS69Md0H5SLM+yow+PxMGCADXNmWr5YBt3NBr6NU/I7tL2qfjPafqq6E9jpvi8WkTVAN+Ay4Gx3s6eBT7Ak2eyUlJRQUlJS57rKykq3L/Z2ul/2AzwDx9PZs4qgekhN9JGYmMjVV1+N3197GDRjWppYbrffAD4D/g3UngM0BiLSCxgFzAc6uwkUVd0pInUOPS0itwC3AOTm5h7Pac0JWL16NR6Pp9a0r6rg9fpIzMii52XT0YFnA8LblUPollDBCzecTk5Ojj1/NM2aOy7FQmC7ql4cbdtYkmSKqh53SU9E0nDm6b5TVYvc+W6PSlVnADMAxowZU/c9n2k0dVXWBAIBApVBNiQNIHjxgzXWeb1eTjtpAN26dWuqEI05Ed8H1uDUsUQVy5/7t0Xka8cThYj4cRLkTFV91V28W0Ry3PU5VH+QZZqN/v37U/0PWtUzyJnPPce2UM3eqSl+Dz07pvLzr9XdNtKY5kREugOTgcdj2b7ekqSIFOPMtS3APSISwGkOJICqatQMLM5v2BPAmiMGw3gTuAH4rfvzjVgCNY0nEgmze9MGNKJ06dsfj9dLdnY2o0aMYOny5ZSUlBx6Bnn6lJvIlwwE8Hs9TD6pC5eO6MaEAdl4PbHdJRjTyLJEZGG1zzPcO9MqfwJ+QoxjUdSbJFX1RAezOAO4DlghIkvdZffgJMeXRORmIA+YfoLnMSdgx7o1vPH73xAMBBABj8/HyPMvZsV/3qe86CCakMzLn85n+/bt/M/vH6K870QWbNlPbscU/uusvozskRnvSzDmSIWqWufw9iJyMVCgqotE5OxYDhZL7fYVwH9U9aD7ORM4W1Vfj7afqs7BKXXWZWIswZnGFSgr45Xf/JJgRXmN5fP+eWj2YN6a/Tmbt27n3u98i5/+8PtNHaIxDe0M4FL3EWISkCEiz0UZWDymZ5L3VSVIAFU9gDOdg2nh1s//3KmujuKi4YO4afwYOhwsJOgOk2ZMS6WqP1PV7qraC7gKpwBYb4KE2JJkXdvEUitumrnykmLCodq12BXBEB+sWkc4EiE9KZHBOZ0Qj4eyogNNH6QxcRZLklwoIg+JSF8R6SMifwQWNXZgpvH1GDIcj7fm37uKYIjHP1vAv1dvIG/vgcMrREht37FpAzSmEanqJ0drIwmxJcnvAZU4/a1fBiqA755YeKY56NK3P31Hj8Wf6EyjUBEM8dhnX5K39wDXjBtF72x3sIrERMZNvQqf9aAxbVAsfbdLcfpXV7VST3WXmVZg8h0/Zu0Xs5n51CyefPMNdu49wMRrv8b0qZtJSl1DeVESXu+1jL10arxDNSYuYhmZ/HkRyRCRVGAV8JWI/LjxQzNNQTweBo8/m2Xdz2RXcRnjb7yMH1y7kZT0CjweSM2sICnjWQoLP453qMbERSy320NUtQi4HHgHyMVp/2haONUwxcVbKSnZwOXj8vjWb6/krivXk+g7YvZgDbBh4+/iE6QxcRZLLbXf7V54OfAXVQ2KiPWlbmFUlaKiJQSDB8jIGMm+fZ+zdNl9/OhHaxg/PoWp09qTO0zrbdhaXr6lKcM1ptmIJUk+ijM47jJgtoj0BGrPCmWaJVWlomIbixZfy571JexekkmwzIe3YxF/fXMNa7+qYOqUDDxH+buXlNSjiSI2pqY9aXn8/bRj6MjwaMOeP5aKmz8Df662aKuIHMvgu6YJLcnbz1/+s5Ye/pmM6/wxfk8F4GPPyjR2zO+ChjxOM5/X15G3t4Kf/6ITE85Kq3EMVWcemyoeTxL9+t7VtBdiTDMRbYCLa1X1ORH5YT2bPFTPchMnn63fw7efWcg1A59mTKcl+D3O1AuRUIjCle3RkIdIRHniswXk7T3ALZcO4swzgjjjmBymQDDsw+8JkZTUlX59f0J29vlNf0HGNAPRSpKp7s8THejCNJFfvrGKBDnAKZ0X4fdGDi0XDwyctoWSHSls+Xc3RuV2Y3z/3gxI7Qysq3WcYDiBv6+4lYeuu4l+nWzqV9O2RRsF6FH35wNNF445XpWhCFv3ltI7o5BIyMPB/GTSu5Xh8SnigfKKMPlF+8g9Vzg92BOASAh2Lcqi66l7Dh1HFb7YeTrfnjjdEqQxRL/d/nN96wBU9Y6GD8ccL79XSPZ72VPWka3vdqPbGbvw+Jzb6LKyCD/72U42b67kmaf8eBNDhAM+xBchrWtZjeNE8PDzqx8lOcG65xsD0W+3q/pnnwEMwemWCM74j9Z3u5lQVXbvfott+U/zvxMKWTw/h/JNSfiTnLlpqhLkmtUBfn5vJ9LTvfjTgmhE6HLqHjJ6HO48FYp4SUg92xKkMdVEu91+GkBEbgTOUdWg+/nvwAdNEp05qnXrH2THjheJRCqIFCSTuiIVVQ/F+akkdd/PPfceTpBnnZWGqtJr4i7a9biErbvnUBFKxOcJEYr4EG9Hzhj9P/G+JGOalViKDF1xKm/2uZ/T3GUmzioqdpKf/xwQRhW2/qcrqAdQdi3OZsmCrYcS5IQJaWjEqcRJal9Bin8n0y/6hDmr/kVF+UYGdh1K3x6TcLrnG2OqxJIkfwssEZGqzrtnAfc3WkQmZrt2vUbVLL+VxX6C5VX/nEKwxM+IpNH88nt5jB7moWibj/SupYgHRBLISB9Kgs/HuSMui1v8xrQER+27rapPAqcCr7mv06puxU18HTi45NB78SioM9zZrPlLOVhWQbgigbSd/fjq1V4kpIbwuDnU40mge4/r4xS1MS1LTE/oVXUXNqths6Cq7CutJMnvxetNPbQ8IS0EKWU8/uFy8vYeYESPHNqlJBESobx/OrSDiHro2H4sAwbcR1JilzhehTEth1VjtiALt+zjx68sJ3+/02zntF4XMrXHR6QllFFWFuGJzxeQt7eC68aPYGj3bBRla2Iv3g1cwAuf+Xj8+lM4uX9WnK/CmJbFkmQLsW1fGdc9sYDyYPjQsi82R1i27R7O6fwubzzyNnnry/n5zzsxoleEXfuUd4q/wVehDvTLSuWeyYMZbwnSmGMWrTF5h2g7quq+aOtNw3r43+trJEiAUEQ5GEjnta/OY/fBz+ly+U10GbGcdh13MHHar/luu9FxitaY1uNojckVZ+7sXGC/+z4TyAN6N3ZwxlEZivDGsu21lkcqyxGvD0npQOfr/oh4vPxx8XhO75POpHMsQRpzJBFJAmYDiTj57xVVjTpFdr2126raW1X7AO8Dl6hqlqp2BC4GXo0hmH+ISIGIrKy27H4R2S4iS93X12K7tLbtjaXbCUdqjtQTCZRR8NJ9FL75O1QV8TjtG4ORBNYVxCNKY1qEAHCuqo4ARgIXisi4aDvEMn3DKar6TtUHVX0Xp63k0TwFXFjH8j+q6kj39U4d6001i/P2c8+ry4mEI3g0DBpxEuTL9xPYsZaUwRMQqTme+OCuNjCFMXVRR4n70e++oo44HUvFTaGI3As85x7sWmBvDMHMFpFeMRzf1ENVueO5hQTDyvmFH9EpUMC8lCF88e5zBHasp9fXbkMHja+1372TB8chWmOajSwRWVjt8wxVnVH1wZ31dRHQD/irqs6PdrBYSpJXA9kcbkye7S47XreLyHL3drx9fRuJyC0islBEFu7Zs6e+zVq13UUBdhdX0KdsM73LttA+VMSOfz1CYMc6rhs3klvS8vFGQjX2GZKTQZ/stHqOaEybUKiqY6q9ZlRfqaphVR0JdAfGisiwaAeLpcfNPlX9PnCmqp6sqneeQM3234C+OM8CdgJ/iHLeGVUXmZ2dfZyna7ki4TBbF80jElEGlawjQZ1keP7Q/lx72ihG9MhBgW4VO2rst76gOA7RGtPyqOoB4BPqfix4yFFvt0XkdOBxnIEtckVkBHCrqt52HEHtrnbcx4C3j/UYrd3mwlJ+/95aZq/KIylQTDpCoLKShVvyGdOrOz06ZNKjQ+ah7fWI55EeqW++Q2OMiGQDQVU9ICLJwCTgf6PtE8szyT8CFwBvAqjqMhGZcJwB5qjqTvfjFcDKaNu3NXl7y7jkkTmUBUJESKA4oSOeiiJe+vg/lO3aSG6HTDplVLuVFmF70uEBmfwe4WvDc+IQuTEtRg7wtPtc0gO8pKpRC2ux9t3edkQNari+bauIyCzgbJyHqPnAfcDZIjISpwJoC3BrLOdvK/7y8XrKK0NUzU4TCZSx65VfE9i1iSlnnE5WRjqK4vP5EK+XBb0vI1kTCIYj+L0eumYmc98lQ+J6DcY0Z6q6HBh1LPvEkiS3ubfcKiIJwB3AmhiCqaty54ljCa6t+XLLfsJuY4TqzXyyLv0JlTkZJJQuZNDpE8jpP5ABp47neympfLahkA0FJfTrlMaZ/bLweOx225iGFEuS/C/gYaAbkI8zKvkxP480R9clI5HNhc50ChV5KwjsXEfWpT8hddB4VkeC9GMft3/rNrw+/6F9zhqQzVkD2l7FljFNJZYkOVBVr6m+QETOAD5vnJDarj7BHcwPe4l4/aT0P5Vut8zAl+EkwLDHz5aBF9ZIkMaYxhdLO8lHYlxmTsC2VctJ+fRJKp+/g/DGefgiQRIzOpAT2Hlom+KIDdpkTFOLNgrQacDpQLaI/LDaqgzAJkJpYB+/8CyPf/wFO/ce4Bvb36Gvfx2JkUo8GuHJ3OsJ+ZOZOKhTvMM0ps2JVjRJwGkb6cOZCKxKETCtMYNqa4qLi3nwiWfJ23uAa8Y5DcUJO88mK8VPWqQUb2o7bpnQJ86RGtP0hgYqWbg5L+btG7rqMtqUsp8Cn4rIU6q6tYHP26bt3ryRef98gb35eWR07c7vXn6TTbv3cO3pJ3NSt5rTKngErpl0MjedNYDMlIQ4RWxM2xXLQ67HRWS624UHt7/1C6p6QaNG1gqFgkGWvv82n816hkgoCMC+ndvxHdzLHx98APlqGcFABapOOyBfYiJnTr2asRdF7VpqjGlEsSTJrKoECaCq+0XEHo4do+Ufvc9H//gbkZDTB7siGKIiGCQzJZkpo4aQVX6AC/77IT5/8Vm2r11NSrtMTr3iSgadflydm4wxDSSWJBkRkVxVzQMQkZ4cZfw1U9O6eZ/z4YzDDQIqgiGe+GwBxRWV/OiCCfi8HnZv3EDHbj249If3xDFSY8yRYkmSPwfmiMin7ucJwC2NF1LLV1Z0kEBpCe06d0HEwwd1JMitbiWNz+u0wkpKs+HNjGmOjpokVfU9ETkZGIdTcfQDVS1s9MhaoIqSEv7159+xbdVyPF4vvoQExl99A4FSZyDkIxPkiB7OYBS+xETGXHxFPEM3xtQjWjvJQaq61k2QAFUDF+a6t9+LGz+8luX13z3IjvVr0XCYcChIMFDBh4/95dD6fy1fUytBisfDSRMvtCRpTDMVrSR5F/Bt6h4YV4FzGyWiFmr/rh3s2rgODR8xQJIefnx70fBBDOvWhYFdDve1vvA7dzJkgn2VxjRX0dpJftv9eU7ThdNyFe8tJBwK1VpeEQzxnzUbOG9of1IS/DUSZFJ6BgNPP7MpwzTGHKNot9tTou2oqkedVrYtEaRGqRGcBPn4ZwvI23uAAV2y6dcpC/EI4vGQ1aMnF9/5UxuwwphmLtrt9iXuz044fbj/434+B2deCEuS1Xi8XpDDibJ6grxm3Cj6deqIPymJ82+9ne6Dh5PWvkOcIzbGxCLa7fZNACLyNjCkatoFEckB/to04bUcnfv2R8SDarhWgqyqpElISmLAuPF4PDY+iDEtRSxDpfWqNi8NwG5gQCPF02L5/H5OudR5QnGgrJy9JWU1EmRq+w5Mu/fXliCNaWFiaUz+iYi8D8zCqdW+Cvi4UaNqoU654usU7SlA5n7G3ZPPITkhEYCzrruZURdMRjyx/E0yxjQnsTQmv11ErsDpaQMwQ1Vfa9ywWp7i4mIuuugizj//fG77f4+wdfkSElJS6D/2dJJSrTeNMS1VrENdLwaKVfXfIpIiIumqWtyYgbUkVQly3rx53HnnnWT16ElWj57xDssYcwQR6QE8A3QBIjiFvoej7XPU+z8R+TbwCvCou6gb8PoJRdoCRMJhdqxby66N69GIM8lrOBSkoqTk0FBmUDNBvvDCC0ybZuMRG9OMhYC7VHUwTlfr74pI1HmYYylJfhcYC8wHUNX1rX2otK0rlvL2n/6XSDiEKviTkug2aAibF39JJBIhJSOToWedy1fzv+C/Z77MlsL9/L9f3MPUqVPjHboxJgq3Enqn+75YRNbgFPxW17dPLEkyoKqVIs6g6CLioxUPlVZ6YD+v/+5BQoHAoWXBinLWzzs8OWTJvkLmv/YSAKf26cEZ/Xrh27yGJe+/zckXXlLrmMaYJpUlIgurfZ6hqjOO3EhEegGjcAuA9YmluvVTEbkHSBaR84CXgbeOtpOI/ENECkRkZbVlHUTkQxFZ7/5sH8P5m9SaOZ+gkeh/AyqCITbt2QfA6J7dGdEjh1AgwNyXZx66NTfGxE2hqo6p9qorQaYB/wTuVNWiaAeLJUn+FNgDrABuBd4B7o1hv6eAC49Ydjfwkar2Bz5yPzcr5UUHCQcr611f1VD88c8WUFIRqLGusqKCQHlZY4dojDkBIuLHSZAzY+leHTVJiogHWKGqj6nqdFWd5r4/6u22qs4G9h2x+DLgaff908DlRztOU+sxbAT+xKQ611XvSfP1U0aQlpRYY70/IZGE5OSmCNMYcxzEeW74BLBGVR+KZZ+oSVJVI8AyEcltgPgAOlf13nF/NrsKoJ7DRtB1wCA8vpqPa+vraljFl5DIqVO+bj1qjGnezgCuA84VkaXu62vRdoil4iYHWCUiC4DSqoWqeukJhXoUInIL7jQRubkNlaNjOK/HwxV338/fbrmGQLWhzxZsyqs3QQL0GjnaBs41pplT1Tkc49TcsSTJB44vnDrtFpEcVd3pDpRRUN+G7sPWGQBjxoxp0tp0r89HZXl5jWXjB/SmT3ZHundoV+c+6R07UtUCwBjTetR7uy0iSSJyJzAdGAR8rqqfVr2O83xvAje4728A3jjO4zS69l26UhEM8dzcxRSWlOIRcRJkHYnQn5RE1/6D4hClMaaxRXsm+TQwBqdW+yLqnsahXiIyC5gLDBSRfBG5GfgtcJ6IrAfOcz83S6OnXMUTc75kef4udh10emB6/X46duuBz59waDuvz0da+470P/X0eIVqjGlE0W63h6jqcAAReQJYcCwHVtWr61k18ViOEw/FxcXc9rN7ydt3kNsuu4gBGSlk5nRl/FXX02PIcOa//jKrPvmQSDjMwNPP5LRp37ARxo1ppaIlyWDVG1UNtZXnbdX7Ys+aNYvp06fX2uaMK6/hjCuviUN0xrQ9K7QPvSr+dAx7XNyg54+WJEeISFVLdMHpcVPkvldVzWjQSJqJiNtjpr4EaYxpW6JN39CmGvwVFxfj9Xpp164ds2fPxmMD5BpjiK1bYqtXdYs9ZcoUVNUSpDHmkFgH3W21jnwG2VaevRpjYtOmi0yxVNIYY9q2Np0kb7jhBkuQxpio2nSSfPDBB3n55ZctQRpj6tXmkmRxcTGPPvooqsrQoUO54goblMIYU782lSSrnkF+97vfZeXKlUffwRjT5rWZJHnkrIbDhw+Pd0jGmBagTSRJm/bVGHO82kSSnDt3LgsXLrQEaYw5Zq26MbmqIiKcf/75bNq0ia5du8Y7JGNMC9NqS5LFxcVMnDiRN95wxvW1BGmMOR6tMklWPYOcPXs2+/fsJlRZ/xSxxhgTTatLkk6CvJC5c+dy3emjKfrsA/7vW99g7iuziGEmXGNMKyci/xCRAhGJqR1gq0qS5eXlTi323Hlcf8YpDMvJJlhRQTBQwYI3X2HZv9+Nd4jGmPh7Crgw1o1bVZJMSkri1FNP5fozxzIsJ6vGulAgwILXX45TZMaY5kJVZwP7Yt2+VSTJ4uJiNm7ciIjwv7/9LcO6ZNW5XfnBg00cmTEmDrJEZGG11y0ncrAW3wSoqpJm27ZtfPXVVyQlJZGR3YmDBbtrbZvdu08cIjTGNLFCVR3TUAdr0SXJ6j1p/vCHP5CUlATAOTfeii8hsdqWgi8hkbOvuzk+gRpjWqwWW5KM1tWw7+ixTL3nAb54ZRb7d2ynU6/enD79Gjr36RfHiI0xLVGLTZK//OUvo/bF7j54GFf+4jdxiMwY05yJyCzgbJxnl/nAfar6RH3bt9gk+eCDDzJ58mQmTZoU71CMMS2Iql59LNvH5ZmkiGwRkRUislREFsa6X3FxMT/4wQ8oKSkhLS3NEqQxptHFs+LmHFUdGWstVNUzyEceeYR58+Y1dmzGGAO0kNrtSCRSo5LGSpDGmKYSr2eSCnwgIgo8qqozjtzAbQB6C0BiYiKhUMjGgzTGNLl4JckzVHWHiHQCPhSRtW5XoUPcxDkDICEhQS1BGmPiQeI9Mo6I3A+UqOrvo2yzB9jaZEFFlwUUxjuIZsK+i8Psu3D0VNXshjygiLyH8/3GqlBVYx7A4qjnb+okKSKpgEdVi933HwK/UtX3mjSQ4yQiCxuyy1NLZt/FYfZdtF7xuN3uDLwmIlXnf76lJEhjTNvT5ElSVTcBI5r6vMYYczxaRBOgZqZWTXwbZt/FYfZdtFJxr7gxxpjmzEqSxhgThSVJY4yJwpJkFHXNqiYiHUTkQxFZ7/5sH88Ym4KI9BCRj0VkjYisEpHvu8vb4neRJCILRGSZ+1084C5vc99FW2FJMrqnqD2r2t3AR6raH/jI/dzahYC7VHUwMA74rogMoW1+FwHgXFUdAYwELhSRcbTN76JNsCQZRT2zql0GPO2+fxq4vCljigdV3amqi933xcAaoBtt87tQVS1xP/rdl9IGv4u2wpLkseusqjvBSR5ApzjH06REpBcwCphPG/0uRMQrIkuBAuBDVW2z30VbYEnSxExE0oB/AneqalG844kXVQ2r6kigOzBWRIbFOSTTiCxJHrvdIpID4P4siHM8TUJE/DgJcqaqvuoubpPfRRVVPQB8gvPcuk1/F62ZJclj9yZwg/v+BuCNOMbSJMTpaP8EsEZVH6q2qi1+F9kikum+TwYmAWtpg99FW2E9bqKoPqsasBu4D3gdeAnIBfKA6ap6ZOVOqyIi44HPgBVAxF18D85zybb2XZyEUzHjxSlkvKSqvxKRjrSx76KtsCRpjDFR2O22McZEYUnSGGOisCRpjDFRWJI0xpgoLEkaY0wUliRPgIhcISIqIoNi2PZOEUk5gXPdKCJ/iWG7LSKS5b7/4jjOc7+I/Oh4YmzJRORxd9AOROSe49g/WUQ+FRHvcey7zG1uVn3ZJyJSa2IxERkuIk8d6znM8bMkeWKuBuYAV8Ww7Z3AcSfJ46Gqpzfl+cDp19zU52wIqvotVV3tfjzmJAl8E3hVVcPHspOIDMb5PZzgzh4alaquALqLSO5xxGiOgyXJ4+T2Yz4DuJlqSdId/OD3IrJCRJaLyPdE5A6gK/CxiHzsbldSbZ9pVaUDEblEROaLyBIR+beIdD5KHB1F5AN3+0cBqbauxP2ZIyKzRWSpiKwUkTPd5ReKyGK3JPNRtcMOcUsym9zYq473uogscsdRvKX6eUTkVyIyHzhNRG4WkXXuMR6rKgG7vVX+KSJfuq8z6rieWt+fu/yX7j4rRWSG2wuoqsT1JxH5wl031l0+1l22xP058CjH/0RExojIb4Fk97uaKSIPijt+prvdb6p/J9Vcg9vLRkTOdkuVL7nfw29F5BpxxqFcISJ9q+33DeBZ4APg0iOOOd3dZ13Vv5nrLWL7w2wagqra6zhewLXAE+77L4CT3fffwenj7HM/d3B/bgGyqu1fUu39NOAp9317Djfy/xbwB/f9jcBf6ojjz8Av3feTcYbtyqp+DuAu4Ofuey+QDmQD24DeR8R5v3s9iTg9jfYC/iO2SQZWAh3dzwpc6b7v6l5rB5xhxD6riht4Hhjvvs/F6eZ45PXU9/11qLbNs8Al7vtPgMfc9xOAle77jGrHmAT88yjH/wQYU8e/TS9gsfveA2ysuu5q2yQAu6p9Phs4AOS43+N24AF33feBP1Xbdh3QEzgfeLPa8k+q/dt/Dfh3tXVnAG/F+3egrbziMe92a3E18Cf3/Qvu58U4v5B/V9UQgB5717TuwIviDJKQAGw+yvYTgCnuuf4lIvvr2OZL4B/iDFLxuqouFZGzgdmqurmOOP+lqgEgICIFOHOl5wN3iMgV7jY9gP44STSMk3gAxgKfVh1PRF4GBrjrJuGUUqvOkyEi6eqMUUm1ber6/s4RkZ/gPLLoAKzCKVEBzHK3nS0iGeL0rU4HnhaR/jhJ3H+U49dJVbeIyF4RGeV+D0tUde8Rm2XhJMXqvlR36DQR2YhTUgSna+c57vJTgD2qulVE8nH+jdqratW/YdVAIotwknWVApw/RqYJWJI8DuL00z0XGCYiilM6U/eXWHB+KY+m+jZJ1d4/Ajykqm+6iez+YzxW7ZVO8piAU9J8VkR+h/NLXd9+gWrvw4DPjWUScJqqlonIJ9XirtDDz+KE+nnc/cujbFPr+xORJOD/cEp620Tkfmp+Z0dehwIPAh+r6hXijIH5SX3Hj8HjOCX5LsA/6lhffkQ8UPM7jFT7HOHw793VwCAR2eJ+zgCmuuerfowwNX9Xk9xzmiZgzySPzzTgGVXtqaq9VLUHTolvPE6J4b9ExAfO3CfuPsU4pZsqu0VksIh4gCuqLW+Hc3sGh0eViWY2zvMwROQinNv1GkSkJ1Cgqo/hjOZzMjAXOEtEeh8RZ33aAfvdBDkIZxqHuixwj9ve/Q6mVlv3AXB7tbhG1rF/Xd9fVQIqFOdZ8LQj9vm6u+144KCqHqTm93jjUY5/pKBb6q7yGs5waKcA7x+5sVvy87rJPCbuv/t04CT3/1AvnNHNr45h9wE4jztME7AkeXyuxvnFqe6fOA/hH8cZBWa5iCxzl4Ezef274lbc4MyB8jbwH2BntePcD7wsIp8BhTHE8gBOzehinOdaeXVsczawVESW4CSth1V1D3AL8Kob54tHOc97OCXK5TiltHl1baSq24H/xhkh6N/AauCgu/oOYIxbYbIa+K86DlHr+1Nn3MbHcG5VX8d5fFDdfnGaO/0dpyIN4P8B/yMin+OU9Os9fh0xzHDXz3SvqRL4GGfEn/pqrz/A+SMZqwnAdvf7qjIb53FEzlH2PQf41zGcy5wAGwXINDgRSVPVEre09hrwD1U98o9KQ53rE+BHqrqwMY7vnsOD87x5uqqur2ebUcAPVfW6xorDPU8i8ClOBVioMc9lHFaSNI3hfnHmgFmJ8xji9bhGcwLEaWC+AWcmxDoTJICqLsFp4tXY7URzgbstQTYdK0kaY0wUVpI0xpgoLEkaY0wUliSNMSYKS5LGGBOFJUljjIni/wN8J4Ag+PRyXwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Train\n",
    "regressor.fit(x_train, y_train)\n",
    "# Test\n",
    "y_test_pred = regressor.predict(x_test)\n",
    "print(\"MAE: %0.2f, R^2: %0.2f\" % (mean_absolute_error(y_test, y_test_pred), r2_score(y_test, y_test_pred)))\n",
    "# Note we can access the seriesIdx using the indices of the test dataframe, as sklearn plays very nicely with pandas\n",
    "plotyy(y_test, y_test_pred, color=eis_data.seriesIdx[y_test.index])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is only a tiny bit worse. Our simple data exploration seemed to suggest that this should be challenging task.\n",
    "\n",
    "Here, it turns out that our test set is actually not very difficult - we are simply picking points at random, regardless of what cell they are from. In the 'real world', we need to predict the capacity of cells that we have no capacity data for."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predicting capacity from unseen cells\n",
    "When predicting from new data, we have the additional complication of having to 'train' any data transformations, and then replicated them on the test set. For instance, we should normalize our data only using information from the training set, and then transform the test set data accordingly. This practice prevents 'data leakage', e.g., spurious correlations - we don't want **any** information from the test data to affect our model during training; if we normalize the entire data set at once, we are including statistical information about the test data into the normalization statistics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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iUkdgAvA9YDzwNDAa+DLuq8Au+eb2B0NeoJxZvXo106dPp3//ZD5sFdOQl+Z0nCONo8ysv99iKzyAmS0DpgDHA+8CD/nu/wu4F0mXQuoLBJImlUpx4YUXsmzZMtq1a5eYwjeGOEo/F+hYaMWSuvoWHkntcG63XgMmAgN8+m5Aa+CjQusPBJIilUoxaNAgbr31VmbMmJG0OAUTZ0beL4HZkubiLPIAmNngBsptD9wtqQb3crnfzB6R1Br4k69vHTAqLNsNVApphZ82bRrjx49n4MCBSYtUMHGU/m7gV8ArbBzTN4iZvQzslyV9HTAybj2BQLmQqfCVYLTLRhyl/8jMbiy5JIFAmfPJJ5+wZMmSilZ4iKf0L0r6JfAwm3bvZ5VMqkCgjFi1ahVt27alZ8+ezJs3jzZt2iQtUpOIo/TpLvrBkTTDG+MCgZZMuku/1157ccstt1S8wkMMpQ+r6gLVSnQMP3r06KTFKRr53GWN9CGtcp3vLenw0ogVCCRLSzHaZSNfS78t7lPdi8CLbAxrtStwFO7beoNz8AOBSsPMOOWUU1qkwkMD03D9N/YBwGG47+6rgfnA42b232aRkDANN9D8PPHEE6xYsaIiFL7Qabh5x/Rmth54ym+BQIsmlUoxdepUTjrpJI4//vikxSkZIT59IMDG5bGnnnoqixYtSlqckhLLMWYg0JLJXA/fvXv3pEUqKQ229H5cHwi0SCrNAUYxiNO9f0vSWEl9Sy5NINDMPPjgg1Wl8BCve783zhX2nf67/Z+A8Wa2vKSSBQLNwFlnncVBBx3EHnvskbQozUYcH3kpM7vDzA4FfgL8HFgi6W5Ju5ZcwkCgyKRSKQYPHszs2bMBqkrhIeaYXtJgSROAG4DfArsAfwceK7F8gUBRSY/hH3vsMRYuXJi0OIkQp3v/JjAZGGtmz0fS/yrpyNKIFQgUn0yj3SmnnJK0SIkQR+nPNLPnogmSDjOzf5nZ90skVyBQVFasWFF1VvpcxLHeZ3OgcVOxBQkESkltbS1dunSpeoWH/AEsDwEOBbpK+mHkVAcgfLsPVASpVIq6ujo6d+7MhAkTkGJHZmux5Ovetwa28nm2jqQvB6r7VRmoCNJj+HXr1jFt2jRqakJbBflj2U0Fpkq6y8zeaUaZAoEmk2m0Cwq/kXzd+9+b2RjgZkmbrb+N4QI7EEiEapxaWwj5uvf3+L+/aQ5BAoFi8d3vfjcofB7ixLJrD6w2s3p/XAO0MbNVDZRrCzwDtMG9XP5qZj+PnP8xMBboamZ5I9wEJxqBXEycvZixT77Oe8tWs0PHdlw0cHcO6FLPnDlzOPnkk5MWr1koRSy7fwJbRo7bAf+IUS4dn34fYF/geEkHeyG7A18Bms37TqDlMXH2Yn760CssXraa9WtX8eoT93DJX+fw4ketqkbhG0McpW8biTOP398yT/50vlzx6QF+h5vHH8JZBRrN2CdfZ3XdeurXrmLpA1ew7Jk/89mi1xn75OtJi1bWxFH6lZL2Tx9IOgDnK69BssWnlzQYWGxmLzVQNsSnD+TlvWWrNyj82vdeo8vgn9Bmh915b1msn2fVEmca7hjgAUnv+ePtga/Gqdz72Ns3HZ9e0t7Az4DjYpS9Hbgd3Jg+zvUC1UW3dsacezcqfPs+ziP7Dh3bJSxZeRMn2MW/JfUBdgcEvGZmdYVcxMyWSZoCDAF2Bl7yM6N2AmZJOtDM3i9U+EB1M6xXPf/+cOEmCt+utoaLBu6esGTlTVwfebsDfXF+7/eThJn9OV8BSV2BOq/w6fj0vzKz7SJ53gb6NWS9DwSirF+/npqaGi4eNZjte7zAbTOWbmK9H7rfjkmLWNY0qPSSfg4cjVP6x4ATgOeAvEpPjvj0TZI2UPWkUilOPPFERo4cyXnnnceZ/ffkzBB4rSDiGPJOA44B3jezbwL74L6958XMXjaz/cxsbzPb08x+kSVPr9DKB+KSnmn3/PPP06lTp6TFqVjiKH16Ys7nkjrgLPG7lFasQGBTolNrx40bx/Dhw5MWqWKJM6af6a3vd+Bi2q0AXiilUIFAlLq6uqDwRSSO9f67fvdWSU8AHczs5dKKFQhspLa2liFDhjB69Oig8EUglvVe0inA4bgZdM8BQemriGzz25vDQp5KpViwYAH77LMPF110UcmvVy3Esd7fggtPPc4nfVvSsWZ2QUklC5QF6fntq+vWA7B42Wp++tArACVV/PQY/rXXXmPBggV06NChZNeqNuK09EcBe5pfjifpbuCVkkoVaDTFbpXT89ujrK5bz9gnXy+Z0meuhw8KX1ziKP3rQA8g7T2nO6F7X5aUolXONY+9VPPbgwOM0hPnk922wHxJU/xU2ldxzjIflvRwSaULFES+Vrmx5JrHXqr57WPHjg0KX2LitPSXl1yKQFEoRat80cDdN+k9QGnnt1922WV85Stf4YgjjihJ/YF4n+ymNocggaazQ8d2LM6i4E1pldPDglJa71OpFD/84Q+59tpr6dq1a1D4EhPHen8wLrjFHji32DXASjML1pUyo1St8tD9dmwWo92wYcMYNGhQSa4T2Eic7v3NuFDVDwD9gDOBL5ZSqEDjaI5WuZhkGu2CwjcPsSbnmNlbkmq8U4z/lfR8g4UCiVDKVrmYBCt9csRR+lWSWgNzJP0aWAK0L61YgTRJzYYrNStWrGDZsmVB4RMgjtJ/A/dp70LgB7jv9KeWUqiAI6nZcKVk5cqVtGnThu23357Zs2dTW1ubtEhVR5zv9B8B68xsuZldCVwEvNdAmUARKMV39yRJpVIMHDiQs88+GyAofEKU0u99oIk092y4UhIdww8eHCKiJUnJ/N4Hmk5zz4YrFcFoV16U1O99oGlcNHB32tVuGm21Er29nn766UHhy4iS+r0PNI1K++6ei0svvZRzzjknKHyZ0GAASwBJtTTB731TCQEsK49UKsUTTzwRPN00A4UGsIw7OacOmNtoqQJVRSqVYtCgQUyfPp0DDjiAXXYJflTLibjBLgKBWKQVftq0aYwfPz4ofBkSx5DXKCS1lfSCpJckzZN0pU8fK+k1SS9LmuA97QZaAJkKH8bw5UmDSi/HSEmX++Mekg6MUXeu+PRP4dxv7Q28Afy00dIHyorHHnssWOkrgDgt/S3AIcAIf5wC/tBQoVzx6c1skpl97tOn44JYBloAX/3qV5k/f35Q+DInjtIf5D3frgEws09x6+obJFt8+owsZwOP5yjbIuLTT5y9mMOue5qdL3mUw657momzFyctUlFJd+mff94tvNx1110TlijQEHGUvs4HoUx7w+0K1Mep3MzWm9m+uNb8QEl7ps9J+hnwOXBfjrK3m1k/M+vXtWvXOJcrO9ILZhYvW42xccFMS1H89Ey7SZMmsWTJkqTFCcQkjtLfCEwAtpN0DS7YxbWFXMTMlgFTgOMBJI0CTgLOsDgTBSqUlrZgJkrm1NpTTw0LLyuFOD7y7pP0Ii5yrYChZja/oXK54tNLOh64GDjKzFY1TfzypiUtmImyYsWKMJe+gonjI68zbkw+LpJWG2NWXtb49JLewoW6fkoSwHQzO7+xN1DOlMJRZTnQtm1bevbsyZgxY4LCVyBxJufMwjnO+BTX0ncElkhaCpxrZi9mK+SDXO6XJb1qLD3N7T661KRSKVatWkW3bt24776spphABRBH6Z8AJpjZkwCSjsONze/Hfc47qHTiJUOxXFTlWjADcNh1T1fUIpr0GH758uXMmjWLLbYIkzkrlQYX3EiamTmZP50maY63zpeU5lxwk+miClzr/MtT9sqqmLleEPnSM+uvrRHtW2/BZ6vryvIlENbDlzelWHDziaSLgfH++KvAp36sHuvTXSVxxcPzYgdszOXDbuY7n/Dgi4uz+rbLZtGvW28sW123Wd5yUPyg8C2POJ/svo77zj7Rb919Wg1weqkES4KJsxdvUL5MFi9bvdnkmlyf5MbNWJTzxRHHcl9On/XGjBkTFL6Fkbel9635781sZI4sbxVfpORoSNEyW+FcCrw+x5Ap3dXPZtHPlrccuPbaazn11FNDIIoWRN6W3ge36Or93rd4Cm2Fc316q3GfIjcjPV7PdIGVK29SpFIprrzySurq6ujWrVtQ+BZGnO7928C/JP0/ST9MbyWWKxHiKlr65ZDLh92Ig7rn9G03dL8d+eUpe7Fjx3YI6LRlLbWtlDVvEqTH8FdddRUzZmQulQi0BOIY8t7zWytg69KKkyzZvqtnI/1yyOfDrl/Pzjk/+2WGniqXKDaZRrvDDz+82WUIlJ5YPvKSprk/2aUVsOOWtaxY8zl19RufUfrzHWT//l4OytsYgpW+cin0k12c7/RdgZ8AXwLaptPNbEBjhSyUJB1jZmuFgazf2jGyviAqQfHnzJnDMcccw2233RYUvsIohdJPAv4C/Bg4HxgFfGhmFzdF0EIoN2+4h133dCwLPMCOHdvxr0ua7f1YMHV1dRvCS3322Wdss802CUsUKJRClT6OIW9bM/sjbsXcVDM7Gzi40RK2AAr5nFYun96ykUql6N+/P9dffz1AUPgqIZYTDf93iaQTJe1Hlbu4KuRzWrmuqIuO4Xv06JG0OIFmJI71/mpJ2wA/Am4COuBCVlcd6fH94mWrEd6VkCfXmL4cV9QFo111E8eJxiN+9zOgf2nFKV8y59kbbFD8HSvIer9+/foNgSiCwlcncZxodAXOBXpF8/uxfdWQbZ59WuGjhrpyU/JMampqOOOMMxg9enRQ+ColTvf+b8CzuJj0+WettGAq3fVVKpVi/vz5HHjggZx/fot0VBSISRyl37I5P8+VK7kWyrSSmDh7cVm38Okx/CuvvMLChQvp3Llz0iIFEiSO0j8iaZCZPVZyacqEbBNyck3RXW9WVuvfM4ka7caNGxcUPpD7k52klKTlwGic4q+WtDyS3iK5bOIr/OAvczbzVQ/wy1P2yrqCrpzWv0fJVPgQNjoAeVp6M2vRi2uin99qJNab0WnLWj5dtbkTjbRS/+uSAfzgL3Oy1leOY/ubbropKHxgM+IEsBzmv9OnjztKGlpSqUpMNPIMbHR6kU3h06SVOtdkm3KchHPxxRfz7LPPBoUPbEKcGXk/N7PP0gc+Ws3PSyZRCciMJ5fND15DpJU61xr6cpmEk0qlGDVqFIsXL6ampoZDDjkkaZECZUYcpc+WJ873/Vzx6TtLekrSm/5vp0KFLoRs8eRy+cHLhWCDUmc6wdixY7uyWUmXHsPfd999zJ49O2lxAmVKHOv9TEnX48JTG/A9IGuAiwzS8elXSKoFnpP0OHAK8E8zu07SJcAluDBXJSHbpJpCOePgHpsodaYTjHIgc2rtSSedlLRIgTIlTkv/PWAdbnntA7iQ1Rc0VChXfHpgCHC3T78bGFqYyIXRVAObBFcP3atI0pSGMJc+UAhx5t6vxLXGae+47X1ag/j8LwK7An8wsxmSupnZEl/3Eknb5Sh7HnAe0KRVYHG9z+aiAhwLsXbtWtasWRMUPhCLONb7/5PUQVJ7YB7wuqSL4lSeLz59jLJFiU+fzfBWW6PNnFHmIpdn23JgxYoVrFu3ji5dujBjxoyg8IFYxOne9zWz5bhu+GNAD+AbhVwkIz79B5K2B/B/lxZSV6FkM7yNPW0fxg7fJ5ZCjzioeynFazSpVIrjjz+eESNGYGbU1DTsVjsQgHiGvFpviBsK3GxmdZIa7PTmik8PPIxzuXWd//u3xgofl1yGt1wTbdKMPLhHWY7nM8fwKuPeSKD8iKP0t+F8378EPCOpJxBnGm6u+PTTgPslnQP8F0hs5ki+8X771jUVofChSx8olDiGvBuBGyNJ70hq0JlGnvj0HwPHFCJkNorhK75/n67cO/2/m6ULuGZY+Sk8wNe//vWg8IEmkVPpJY00s3vzRLO5vkQyNUiuaLFQ2Eq3CbMWZ02vrVHZfYdP8/Of/5yzzjqLU089NWlRAhVKvpa+vf9bdgtvckWL/eH9c7ji4Xmx47yvXJd90s669eX1nS6VSjFhwgTOPPNM+vXrR79+sb0dBwKbkW+V3W3+75XNJ048ck24qTdix3mPhpwuZ6Jj+AMPPJA+ffokLVKgwsnXvb8x1zkAM/t+8cWJR9wJN+klsZlKnx4e5KJcbOGZRrug8IFikO87/Yt+awvsD7zpt31J2Fde/z7xJ+tk6xU0NB//jIOT9wMfrPSBUpGve383gKSzgP5mVuePbwUmNYt0OXj05SWx82Zb555vPn65fJufPHkyL7zwQlD4QNGJ851+B5wx7xN/vJVPS4SJsxfndXYRJdc691zDgx07tktc4c0MSQwePJg333yTnj17JipPoOURZxrudcBsSXdJuguYBVxbUqnykM8XXStBx3a1Da5zL1dHGKlUiuOOO46nnnoKICh8oCTEmZzzv34d/EE+6RIze7+0YuUmX9f8+tP3jfV9PZ2nnKLRRMfw3/72txOTI9DyidO9xyt5yefIxyFX17xju9qClLacHGEEo12gOYnTvS8rcnXNrxj8pYQkahorV64MCh9oVipO6cvZR11jaNeuHXvuuWdQ+ECzIcvhGkZS3lAoZvZJvvPFpF+/fjZz5szmulyzkEqlWLZsGd27l+d6/UDlIOlFM4s9NzvfmP5FNkZk7gF86vc74pbE7tx4MaubVCrFoEGD+OCDD5g7dy6tW7dOWqRAFZFvcs7OsGEyzsPpWHaSTsA5xAg0grTCT5s2jfHjxweFDzQ7ccb0X44GrzSzx4GjSidSyyVT4cMYPpAEcT7ZfSTpMuBeXHd/JPBxSaVqoVx88cVB4QOJE6elHwF0BSb4ratPCxTINddcw6OPPhoUPpAoDSq9mX1iZqOBI8xsfzMb05yW+0onlUrx05/+lDVr1tCpUycGDhyYtEiBKieO3/tDJb0KvOqP95F0S8klawGkZ9qNHTuW6dOnJy1OIADE697/DhiIH8eb2UvAkaUUqiWQObX26KOPTlqkQACIOSPPzBZlJCXqRKPcCXPpA+VMHOv9IkmHAiapNfB9YH5pxapsFi1axFtvvRUUPlCWxFH684EbgB2Bd3Fec77bUCFJ3YE/A18A6oHbzewGSfsCt+LccH0OfNfMXmiU9GXG2rVradOmDX379uU///kP7du3b7hQINDMxOne725mZ5hZNzPbzsxGAnvEKPc58CMz2wM4GLhAUl/g18CVPrDl5f644kmlUhxzzDH84he/AAgKHyhb4ij9TTHTNsHMlpjZLL+fwg0JdsRN8Ongs20DvBdP1PIlOobv27dv0uIEAnnJ5wL7EOBQoGtGlJsOQEEhUiX1woW4mgGMAZ6U9BvcS+fQHGWKEp++1ASjXaDSyNfSt8Y5wdwC5xgzvS0HYv+yJW0FPAiM8SGvvwP8wMy6Az8A/pitXLHi05eS+vp6TjrppKDwgYoi53r6DRmknmb2TqMqdyGuHwGeNLPrfdpnQEczM7kYy5+ZWYd89ZTzevp7772Xtm3bBoUPJEah6+njjOnvlNQxcoFOkp6MIYhwrfj8tMJ73mPjKr0BuAAaFUUqleLZZ58FYOTIkUHhAxVFnE92XcxsWfrAzD6VtF2McocB3wBekTTHp10KnAvcIGkLYA1+3F4ppMfwc+bMYeHChZTr0CMQyEUcpa+X1MPM/guuu4+zwOfFzJ4jd1i4A+KLWD5kGu2CwgcqkThK/zPgOUlT/fGRVFjrXAyClT7QUogT7OIJSfvjJtgIZ3n/qOSSlRl33nlnUPhAiyCfN9w+ZvaaV/jNSE+8aQ7KwXpfX1/P7NmzOeCAihyZBFowxfSG+yOc0e23Wc4ZzvLeokmlUpx77rlcc8019O7dOyh8oEWQzxvuuf5v/+YTp3yIjuFHjBhB7969kxYpECgK+abhnpKvoJk9VHxxyoOowo8bN44hQ4YkLVIgUDTyde9P9n+3w82Pf9of9wemAC1S6TMVfvjw4UmLFAgUlXzd+28CSHoE6GtmS/zx9sAfmke85qe+vh4gKHygxRLnO32vtMJ7PgB2K5E8iZFKpaipqWGbbbbhmWeeoVWriovtGQjEIo7ST/Fz7cfhrPZfAyaXVKpmJt2l32qrrXj88ceDwgdaNHEm51woaRgbPeDebmYTSitW85E5hnfrhAKBlkuclh5gFpAys39I2lLS1t4bTkUTjHaBaiROsItzgb8Ct/mkHYGJJZSp2Rg1alRQ+EDVEWfwegFumexyADN7E/cZr+K56qqreOCBB4LCB6qKOEq/1szWpQ/8OvgGl9aWK6lUittuuw0z40tf+hLDhg1LWqRAoFmJo/RTJV0KtJP0FeAB4O+lFas0pMfwF1xwAXPnzk1anEAgEeIo/cXAh8ArwLeBx4DLSilUKchcD7/XXnslLVIgkAh5rfeSWgEvm9mewB3NI1LxCQ4wAoGN5G3pzaweeElS+Tqej8G0adOYOXNmUPhAgHjf6bcH5kl6AViZTjSzwSWTqkiYGZI47rjjWLBgATvssEPSIgUCiRNH6a8suRQlIJVKMWTIEEaPHs2QIUOCwgcCnpzde0ltJY0BhgN9gH+Z2dT01lwCNob0GP6ZZ56hrq4uaXECgbIi35j+bqAfzmp/AtndZpUdwWgXCOQnn9L3NbORZnYbLnbdEYVULKm7pMmS5kuaJ2l05Nz3JL3u04sWqnr16tVB4QOBBsg3pt/QLzazzxux+iwdn36WpK2BFyU9BXQDhgB7m9namNFyYtG2bVsOPvhgxowZExQ+EMhBPqXfR9Jyvy/cjLzlft8aCjrpHW8s8fspSen49OcC15nZWn9uaRPvgVQqxdKlS+nduze/+c1vmlpdINCiydm9N7MaM+vgt63NbIvIfl6FzyQjPv1uwBGSZkiaKunLOcqcJ2mmpJkffvhhzrrTY/gBAwawZs2aQsQKBKqSuOvpG01mfHq/YKcTLmLOl4H7Je1iGVE3zOx24HZwwS6y1Z1ptGvbtm1J7yUQaAmU1C+Uj0//IHBfxGX2u8BD5ngBqAe6FFp3sNIHAo2jZEqfJz79RHx0HEm7Aa2BgmPjXX755UHhA4FGUMrufa749H8C/iRpLrAOGJXZtY/DVVddxYknnsixxx5bLHkDgaqgZErfQHz6kY2pM5VKcfnll3PVVVex1VZbBYUPBBpBxfh6To/hb7rpJqZPn560OIFAxVIRSl9fX7+J0S608IFA4yn5J7ti8MYbb7BmzZpgtAsEikBFtPTr1q0LCh8IFAk1wnDe7Ej6EHgnaTlw8wkK/rzYjAT5mkalytfTzLrGraQilL5ckDTTzPolLUcugnxNo1rkq4jufSAQKB5B6QOBKiMofWHcnrQADRDkaxpVIV8Y0wcCVUZo6QOBKiMofSBQZVS90udy4CnpL5Lm+O3tyErBzPJvS3rF55tZAvnaSnpB0ktevit9emdJT0l60//tlKP88d4J6VuSLmlG+cZKek3Sy5ImSOqYo3xSz+8KSYsj/+NBOcon9fxK9/szs6recBF89vf7WwNv4DwBR/P8Frg8R/m3gS4llE/AVn6/Fudy7GDg18AlPv0S4FdZytYA/wF2wfkteCnz3koo33HAFj79V9nkS/j5XQH8uIGyiT2/Uv7+qr6lN7MlZjbL76eAtANPYIMzkNOBcQnJZ2a2wh/W+s1wHoXv9ul3A0OzFD8QeMvMFpjZOmC8L1dy+cxskpl97tOnAzsV87pNlS9m8cSeX/p8KX5/Va/0UTIceKY5AvjAzN7MUcyASZJelHReieSq8d27pcBTZjYD6GbO4zD+bzZX4jsCiyLH7xJ5oZVYvihnA4/nKJ7U8wO40A8//pRjeFQOz6/ov7+g9J5MB56RUyPI/5Y9zMz2x0UBukDSkcWWzczWm9m+uNbyQEl7xiyazYlJ0b/R5pNP0s9wMRDuy1E8qef3P0BvYF+cq/ZsEZwSf36U4PcXlJ6cDjzxnntPAf6Sq6yZvef/LgUm4LqEJcHMlgFTgOOBDyRt7+XcHtdKZPIu0D1yvBPwXjPJh6RRwEnAGeYHoFnKJPL8zOwDr2z1wB05rpv08yvJ76/qlT6PA0+AY4HXzOzdHGXby0XvQVJ7nPFqbpHl65q2fEtql5YJeBgY5bONAv6Wpfi/gS9K2llSa+BrvlzJ5ZN0PHAxMNjMVuUom9jzS78wPcNyXDex5+dPl+b3V0xLZCVuwOG4LtvLwBy/DfLn7gLOz8i/A/CY398FZ9F9CZgH/KwE8u0NzPbyzcVbcYFtgX8Cb/q/nTPl88eDcF8k/tPM8r2FGw+nn+mtZfb87sEFZ30Zp8jbl9PzK+XvL0zDDQSqjKrv3gcC1UZQ+kCgyghKHwhUGUHpA4EqIyh9IFBlVKXSSxomyST1iZF3jKQtm3CtsyTdHCPf25K6+P3nG3GdKyT9uDEyVjKS7pTU1+9f2ojy7SRNlVTTiLIvSRqXkTZF0mbOKyXtJemuQq9RCqpS6XFTG5/DTbZoiDFAo5W+MZjZoc15PXDzv5v7msXAzL5lZq/6w4KVHrcu4CEzW19IIUl74PTnSD8xJi9m9gqwk6QejZCxqFSd0vs59ocB5xBRer/o4Td+bfLLkr4n6fu4yRCTJU32+VZEypyWfntLOlnSDEmzJf1DUrcG5NhW0iSf/zYi87zT15C0vaRn/FrpuZKO8OnHS5rlW5p/Rqrt61uaBV72dH0T/YKMedFFGZJWSPqFpBnAIZLOkfSGr+OOdA/Fzxp7UNK//XZYlvvZ7Pn59Mt9mbmSbvczINMt4u8lPe/PHejTD/Rps/3f3Ruof4qkfpKuA9r5Z3WfpKvkfSP4fNdEn0mEM/CzGSUd7Vv9+/1zuE7SGXLr3V+R1DtS7uu4CT6TgMEZdQ73Zd5I/888fydeQ1Naij3DqNw3XMTcP/r959m4lv47uPn36TXg6RlubxNZrwysiOyfBtzl9zux0efgt4Df+v2zgJuzyHEjG2eHnYibFdgleg3gR/hZVri13VsDXXEz3XbOkPMKfz9tcEERPgZqM/K0w8362tYfG3B6ZKbX20Bn3PLOZ9NyA/8HHO73e+CmLGfeT67n1zmS5x7gZL8/BbjD7x8JzPX7HSJ1HAs82ED9U4B+Wf43vYBZfr8VbkbdthkytwbejxwfDSzD+VhoAywGrvTnRgO/j+R9A+iJm/r6cCR9SuR/Pwj4R+TcYcDfk9aBiohlV2RGAL/3++P98SzcD+xW82vAzeyTAuvdCfiL3Jzu1sDCBvIfiVtMgZk9KunTLHn+DfxJbkHQRDObI+lo4BkzW5hFzkfNbC2wVtJSoBtu0cj3JQ3zeboDX8S9FNbjFAncQo2p6fokPQDs5s8di+tFpK/TQdLW5vwPEMmT7fn1l/QT3BCpM2666N/9uXE+7zOSOsjNQd8auFvSF3EvpdoG6s+Kmb0t6WNJ+/nnMNvMPs7I1gWn5FH+bX7JsqT/4FpycFN2+/v0LwMfmtk7kt7F/Y86mVn6f5hetPUi7uWTZinu5ZooVaX0krYFBgB7SjJc62n+RyniLZuM5mkb2b8JuN7MHvaKeUWBdW1+0inDkbiewD2SxuJ+pLnKrY3srwe28LIcCxxiZqskTYnIvcY2jmWzLSNN08qXX50nz2bPT1Jb4BZcS7xI0hVs+swy78OAq4DJZjZMzr/BlFz1x+BOXE/rC8CfspxfnSEPbPoM6yPH9WzUlxFAH0lv++MOwKn+etE61rOpjrX110yUahvTnwb82cx6mlkvM+uOa5EPx73Rz5dbzoikzr5MCtf6pPlA0h6SWuFWZ6XZBtcdhI2r3/LxDG48iaQTcMODTZDUE1hqZnfgVgLuD0wDjpK0c4acudgG+NQrfB+cq6hsvODr7eSfwamRc5OACyNy7ZulfLbnl1aoj+RsKZkRSL/q8x4OfGZmn7HpczyrgfozqfO9ojQTcMtUvww8mZnZt8w1/uUUC/9/Hw7s7X9DvXDedEbEKL4bRV5F2BiqTelH4H4IUR7EGWXuBP4LvCzpJZ8GLsDA4/KGPJw/ukeAp3HOF9JcATwg6VniBUG8Emf5nYUbF/43S56jgTmSZuOU8AYz+xA4D3jIy5lzrbXnCVyL/zKuFZ2eLZOZLQauxXkN+gfwKvCZP/19oJ83oL0KnJ+lis2en7n14XfgusYTccOVKJ/KfZ68FWdYBef775eS/oXrieWsP4sMt/vz9/l7WgdMBu633Nb5SbiXflyOBBb755XmGdzwZ/scZdL0Bx4t4FolIayyC2xA0lZmtsK3phOAP5lZ5kuyWNeagnNMWXQPuJFrtMLZa4ZbDndTfsz/QzP7Rqnk8NdpA0zFGUQ/byh/Kam2lj6QnyvkfLXNxQ17JiYqTROQm7DzFvDPXAoPYGazcZ9kSz1PoQfOe3GiCg+hpQ8Eqo7Q0gcCVUZQ+kCgyghKHwhUGUHpA4EqIyh9IFBl/H+QdrONZdj6QwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Use the last cell for testing\n",
    "is_train = eis_data.seriesIdx != eis_data.seriesIdx.iloc[-1]\n",
    "x_train, x_test, y_train, y_test = x[is_train], x[~is_train], y[is_train], y[~is_train]\n",
    "# Standardize the training data\n",
    "scaler = StandardScaler()\n",
    "x_train_ = scaler.fit_transform(x_train)\n",
    "# Train\n",
    "regressor.fit(x_train_, y_train)\n",
    "# Test\n",
    "x_test_ = scaler.transform(x_test)\n",
    "y_test_pred = regressor.predict(x_test_)\n",
    "title = \"Testing on an unseen cell \\n\" r\"MAE: %0.2f, R$^2$: %0.2f\" % (mean_absolute_error(y_test, y_test_pred), r2_score(y_test, y_test_pred))\n",
    "plotyy(y_test, y_test_pred, title=title)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We could also plot the actual and predicted versus cycle count:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1ce3cc664c8>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(eis_data.cycle[y_test.index], y_test, 'ok')\n",
    "plt.plot(eis_data.cycle[y_test.index], y_test_pred, 'dr')\n",
    "plt.xlabel('Cycle count')\n",
    "plt.ylabel('Discharge capacity (mAh)')\n",
    "plt.legend(('Actual', 'Predicted'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We should also always be comparing results with some kind of baseline, to contextualize the error statistics. For this relatively straightforward problem, it's not hard that hard to contextualize the results, but for more complex problems, it can be difficult to understand the error statistics. It's also simply best practice to make sure your models are outperforming extremely simple approaches. There are several 'dummy' model types available in sklearn to compare against."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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SAaWR5TjlpdIGz+qiUM29CeEV2AxgBmuXE9oWOBh4H59j7lQhZsYJJ5xQ0YYNBfy5AeLyuwOB/QlL8i4H5gEPmNmbzaIQ9+d2mp8HH3yQpUuXVoRh5/PnLtjnNrPVwCPx4zhVTSaTYfLkyRx99NEMGjSo3HIaTeNW93acKiHrtjl06FAWLFhQbjlNQiqXT8epZnL9sXv27FluSU1C0Zo79rsdpyppqYEWmoI0zfJXJF0lqW99CpbUXtJTkp6VNFfSZTH9KkkvSHpO0gRJnRsi3HGagrvvvrsqDRuKjJYDSNqIEOL4m4Qfgz8B48xsSZHzBGxoZksl1QJPACOBTsC/zewzSVcCmNnoQmX5aLlTSubNm8dOO+1UbhkNpiHRTwEws4yZ3Wxm+wE/Ai4BFkm6TdK2Bc4zM1sad2vjx8zsYTP7LKZPJcRlc5xmI5PJMGTIEGbNmgVQ0YZdiFR9bklDJE0ArgV+A2wN/A24P8W5zwDvAo+YWa5n+7eAB/Kce6ak6ZKmv/fee8XvxHFSkO1j33///cyfP7/cckpKmtHyl4FHgavM7MlE+v9JOqjQifE9eb/Yr54gaRczmwMg6SLgM2BMnnNvAm6C0CxPodNxCpI7eHbCCSeUW1JJSWPc3zCzJ5IJkvY3s/+Y2ffSXMTMFkuaBAwC5kgaARwNHGrFOv2O0wQsXbq0akfF85FmtLyuQA3XFztJUvfsSLikDsBhwAuSBgGjgSFm9kk9tDpOg6mtraVbt26txrCh8EKA+wL7Ad0lfT9xqBOQ5t335sBt8T15G+AuM/u7pFeAdsAjYUCdqWZ2dkNvwHEKkclkWLVqFV27dmXChAnE71yroFCzvC3QMebZKJG+BCj602dmz7E20EMyPe8Iu+M0Jdk+9qeffsqUKVOoqWld87EKrRU2GZgs6VYze6MZNTlOo8kdPGtthg2Fm+W/M7NRwO8lrTfo5aGNnZZKNU8prQ+FmuV3xL9XN4cQx2kqzjnnnFZv2JBu+umGwHIzWxP3a4B2zTnS7dNPnfqwYMECnnnmGY455phyS2kWGjz9FPgX8IXEfgfgn00lzHGagkwmw5VXXsnq1avp2bNnqzHsQqQx7vaJOeLE7S8UyO84zUq2j33RRRfhLby1pDHuZZL2yO5I2pMQS81xyk5y8Gzs2LF8+ctfLrekFkOa6aejgPGS3or7mwNfLZkix0lJrmEPGzas3JJaFGkWJXha0o7ADoCAF8xsVcmVOU4R5s6dy3PPPeeGnYe0MdR2APoS4pbvLgkzu710shwnP6tXr6ampoZ99tmH+fPns8kmm5RbUoskjT/3JQRHkeuBAcCvAZ/A4pSFTCbDgAEDuOmmmwDcsAuQZkDtROBQ4G0z+yawG8Hxw3GalWwf+8knn6RLly7lltPiSWPc2Qksn0nqRIiqsnVpZTnOuvjgWf1J0+eeHv2ybyasGbYUeKqUohwnyapVq9ywG0Ca0fJz4uaNkh4EOkV3TsdpFmprazn22GMZOXKkG3Y9SDVaLukE4ADACCGK3bidkpPJZHjttdfYbbfduOCCC8otp+JIM1p+A3A2MBuYA5wl6X9KLcxp3WT72IceeihLlhQMke/kIU3NfTCwSzaQoaTbCIbuOCUh1x+7U6dO5ZZUkaQZLX8R6JXY74k3y50S4YEWmo40NfcmwDxJ2RHyvYApku4Dj8jiNC1XXXWVG3YTkca4Ly65CseJ/PSnP+Xwww/nwAMPLLeUiifNq7DJzSHEab1kMhm+//3v84tf/ILu3bu7YTcRaUbL95H0tKSlkj6VtFqSD186TUK2j/3nP/+Zp59+utxyqoo0A2q/B4YT1gzrAJwR0xynUeQOng0ePLjckqqKVJNYzOwVSTVxYb8/S3qy6EmOUwAfFS89aYz7E0ltgWck/RpYBGxYWllOtbN06VIWL17shl1C0hj31wnN93OB8wnvuYeWUpRTvSxbtox27dqx+eabM2vWLGpra8stqWpJY9zvA5+a2Qrgsmzc8tLKcqqRbFN866235vbbb3fDLjEet9xpFpJ97CFDfN5Tc+Bxy52S44Nn5cHjljsl56STTnLDLgMet9wpORdeeCGnn366G3Yz43HLnZKQyWR48MEHGTZsmE8nLRNpJ7GsIgRqcJyiZDIZBg8ezNSpU9lzzz3ZemuPp1kO0vS5G4Sk9pKekvSspLmSLovpXSU9Iunl+Ndj1FYRWcOeMmUKY8eOdcMuIyUzbmAlMNDMdgP6AYMk7QP8GPiXmW1HeM324xJqcJqRpGH74Fn5SeMVJkmnSro47veStHex8yyQfYVWGz8GHAvcFtNvA45riHCn5XH//ff7qHgLQjE0Wv4M0v8Cawi18E6xGf2wme1VtPAwm20GsC3wP2Y2WtJiM+ucyPORma3XNJd0JnAmQK9evfZ844036nFbTrl45ZVX2Hbbbcsto1UhaYaZ9c9NT9Ms/7KZfRdYAWBmHwFt01zUzFabWT9gK2BvSbukFWxmN5lZfzPr371797SnOc1Mtin+5JPBUdANu+WQxrhXxRo4G/20O6EmT42ZLQYmAYOAdyRtHsvanLA8kVOBZGeePfzwwyxatKjccpwc0hj3dcAEYFNJVxAWJfhFsZMkdY/LECGpA3AY8AJwHzAiZhsB3Ft/2U65yZ1SOnSoOwq2NNJMYhkjaQZhpU8Bx5nZvBRlbw7cFmv9NsBdZvZ3SVOAuySdDrwJ+PowFcbSpUt9rngFUNS4JXUlNJ3HJtJqi81Si+uJ7V5H+geEHwqnQmnfvj29e/dm1KhRbtgtmDQz1GYSAjR8RKi5OwOLJL0LfNvMZpROntOSyGQyfPLJJ/To0YMxY8aUW45ThDR97geBwWbWzcw2AY4C7gLOAW4opTin5ZDtYx9++OF89tln5ZbjpCCNcfc3s4eyO2b2MHCQmU3FI7K0CpKDZxdffDEbbJDKJcEpM2n+Sx9KGg2Mi/tfBT6KA2X1eiXmVB4eaKFySVNzn0yYhDIxfnrGtBrgpFIJc1oGo0aNcsOuUApOP421821mdmrzSVqf/v372/Tp08spodXyzjvvMGPGDF8woAXToOmncRGC7jFuudNKyGQyXHbZZaxatYoePXq4YVcoafrcrwP/iUv2Lssmmtk1pRLllI9kH/vQQw/lgAMOKLckp4GkMe634qcNsFFp5TjlJHfwzA27skkz/fSy5hDilBcfFa8+0kw/7Q78CNgZaJ9NN7OBJdTlNDOvvvoq8+bNc8OuItI0y8cAfwWOBs4meHK9V0pRTvOxatUqamtr6devH6+99hobb7xxuSU5TUSa99ybmNkfgVVmNtnMvgXsU2JdTjOQyWQYMGAA11wTxkbdsKuLVMEa4t9Fkr4iaXfCpBangkn2sXv16lVuOU4JSNMs/7mkjYEfANcDnQhL+ToVig+etQ7SjJb/PW5+DAworRyn1KxevfrzBQPcsKubtKPl3wb6JPPHvrdTYdTU1HDKKacwcuRIN+wqJ02z/F7gccKa3KtLK8cpFZlMhnnz5rH33ntz9tlnl1uO0wykMe4vmNnokitxSka2jz179mzmz59P165dyy3JaQbSjJb/XZJ7DlQoycGzW265xQ27FZG35paUIcQqF3ChpJWE12IirBbUqXkkOg0ladhjx45l2DAPNNuayGvcZuZOIhXO9ddf74bdikmzEODx8T13dr+zpONKqsppEkaPHs3jjz/uht1KSdPnvsTMPs7uxKWBLimZIqdRZDIZRowYwcKFC6mpqWHfffcttySnTKQx7rryePjLFki2jz1mzBhmzZpVbjlOmUlj3NMlXSNpG0lbS/otYVlepwWRO6X06KOPLrckp8ykMe7zgE8Jbp/jCUv5freUopz64XPFnbpIM7d8GfBj+Dwa6oYxzWkhrFy5khUrVrhhO+uQZm75XwhBGlYTmuMbS7rGzK4qtTinMEuXLqVt27Z069aNadOmUVNTU25JTgsiTbO8r5ktAY4D7gd6AV8vpSinOJlMhkGDBjF8+HDMzA3bWY80xl0rqZZg3PfGpXvzr2TglJxkH3v48OFIKrckpwWSxrj/QIhdviHwmKTewJJSinLy44NnTlrSDKhdB1yXSHpDkgdtKBMnn3yyG7aTikKOI6ea2Z2Svp8ni684UgYuueQSTjvtNIYOHVpuKU4Lp1CzfMP4d6M8n4JI6inpUUnzJM2VNDKm95M0VdIzkqZL2ruR91D1ZDIZbr/9dgD69+/vhu2kopBX2B/i34auOPIZ8AMzmylpI2CGpEeAXwOXmdkD0U/818AhDbxG1ZPsY++9997suOOO5ZbkVAiFmuXX5TsGYGbfK3J8EbAobmckzQO2JIy0Z33BNyasQ+bUQe7gmRu2Ux8KDahl54/vD/QlTD8FGEY955ZL6gPsDkwDRgEPSbqa0C3YL885ZwJnAq0yrraPijuNxswKfoBHgdrEfi3waLHzEvk7En4MToj71wFD4/ZJwD+LlbHnnntaa+Pee++12tpaGz9+fLmlOC0cYLrVYTdpXDe3IAygfRj3O8a0osTJL3cDY8zsnpg8AhgZt8cDt6Qpq7VgZkhiyJAhvPzyy/Tu3bvckpwKJc0kll8BsyTdKulWYCbwi2InKUyb+iMwz8ySr83eAg6O2wOBl+uluIrJZDIcccQRPPLIIwBu2E6jSDOJ5c+SHgC+HJN+bGZvpyh7f8Ic9NmSnolpFxIWOLhW0gYE99Ez6626Ckn2sc8666xyy3GqgFQRVaIx31ufgs3sCUKk1LrYsz5lVTs+eOaUgjTNcqeELFu2zA3bKQlu3GWmQ4cO7LLLLm7YTpNTaBJLwaUpzOzDQsedwmQyGRYvXkzPnj258cYbyy3HqUKKTWLJrjjSC/gobncG3gS+WGpx1Uomk2Hw4MG88847zJkzh7Zt25ZbklOFFJpb/kUASTcC95nZ/XH/KOCw5pFXfWQNe8qUKYwbN84N2ykZafrce2UNG8DMHmDte2qnHuQatvexnVKS5lXY+5J+CtxJaKafCnxQUlVVyujRo92wnWYjTc09HOgOTIif7jHNqSdXXHEF//jHP9ywnWahqHGb2YdmNhI40Mz2MLNRPlKenkwmw09+8hNWrFhBly5dOPLII8styWklpFnlcz9JzwPPx/3dJN1QcmVVQHbm2VVXXcXUqVPLLcdpZaRplv8WOJLYzzazZ4GDSimqGsidUnrIIYeUW5LTykg1Q83MFuQkrS6BlqrB54o7LYE0o+ULJO0HmKS2wPeAeaWVVdksWLCAV155xQ3bKStpjPts4FpC/LP/Ag8D55RSVKWycuVK2rVrR9++fXn11VfZcMMNi5/kOCUiTbN8BzM7xcx6mNmmZnYqsFOphVUamUyGQw89lJ/97GcAbthO2Ulj3NenTGu1JPvYffv2LbccxwEKe4XtS4hM2j1n1ZFOgC8pGfHBM6elUqjP3ZYQDHED1l1hZAng32BgzZo1HH300W7YTotEITJqgQxSbzN7o5n01En//v1t+vTp5ZSQlzvvvJP27du7YTtlQ9IMM+ufm56mz32LpM6JgrpIeqgpxVUamUyGxx9/HIBTTz3VDdtpkaQx7m5mtji7Y2YfAZuWTFELJ9vHPuqoo3jvvffKLcdx8pLGuNdI+nw9H0m9Ca6frY7k4Nmtt95K9+7dyy3JcfKSZhLLRcATkibH/YNohbHGfVTcqTTSLErwoKQ9gH0IMdTON7P3S66shXHLLbe4YTsVRaH33Dua2QvRsGHtUru9JPUys5mll9d4fjpxNmOmvUnuS4EtO3dgwI7defSF93hr8XK26NyBC47cgeN233KdfBNnLeSqh15k4UfbsvN3fs8G2+zLxFkLufCe5/hk1ZrUOjrUtmHonlutc73s9RcuXk4bwZqosXOHWo7ebfM689a13/kLtZjBx8tXrXMfWe2595cvva77LpQnH405t6GU45qlpCnuJ++rMEk3m9m3JT1ax2Ezs4EN0NwgGvoq7KcTZ3Pn1DdT5+9QW8MvT9j184c49okXOeusM9nogK9T22VzAGprxKrVLXvIoUNtDUP33JK7Zyxk+arVqdKT9z1x1kJ+cs/sgnny0ZhzG0o5rllK6ns/9X4VZmbfjn8H1PFpNsNuDGOn5XqqFmb5qtVc9dCLQOhjn3XqiWTmPcGq99e+5m/phg3hPsZOW7DOl6NYeva+Aa566MWiefLRmHMbSjmuWUqa6n4KNctPKHRiYkneFsvqIhN06uKtxcs/HzzLvPk83Yb8iC9st08J1JWWfPeeL/2txcvr3M6XJx+NObehlOOapaSp7qfQgNox8e+mhDnm/477A4BJQIs37hqp3gbeo4N9Piq+/fCfsrLn3iVSV1ry3Xu+9C06d1hne2EdX6Rknnw05tyGUo5rlpKmup9CzfJvmtk3Ce+0+5rZUDMbCuxcT61lY/iXe9Yrf4faGs4bsA0AY8eO5cofnkmH2nV9ZGpr8i1c2nLoUFvD8C/3XE97ofQLjtzh8/0LjtyhaJ58NObchlKOa5aSprqfNO+5+5jZosT+O8D29bpKmfj5cbsCpBot79HBOP/wbfnqftsy/LHHaNNm7e9e7qglUBGj5f17d61zxDVfepbsdkNGaxtzbkMpxzVLSVPdTxrHkd8D2wFjCbX414BXzOy8hghvCKV2HMn2sTt27MgDDzyA1PJrZ8fJkm+0PM0klnMlHc/aiKc3mdmEphZYLpIzz8aOHeuG7VQNadfnngn8w8zOBx6StFGxEyT1lPSopHmS5koamTh2nqQXY/qvG6i90eQa9rBhw8olxXGanKI1t6RvE+aSdwW2IQRKvBE4tMipnwE/MLOZ8cdghqRHgB7AscCXzGylpLJ5mI0YMcIN26la0tTc3wX2J0RgwcxeJoXLp5ktyk5RNbMMIRzylsB3gF+Z2cp47N2GSW88l19+OePHj3fDdqqSNMa90sw+ze5I2oB6unxK6gPsDkwjjLQfKGmapMmS9qpPWY0lk8nwhz/8ATNj55135vjjj2/OyztOs5HGuCdLuhDoIOlwYDzwt7QXkNQRuBsYZWZLCF2BLgQvswuAu1THKJakMyVNlzS9qYIiZPvY3/3ud5kzZ06TlOk4LZU0xj0aeA+YDZwF3A/8NE3hkmoJhj0mMV31v8A9FngKWAN0yz3XzG4ys/5m1r8pgiLk+mPvuuuujS7TcVoyBQfUJLUBnjOzXYCb61NwrI3/CMwzs2sShyYCA4FJkrYnRFktqX+4B1pwWiMFjdvM1kh6Nvpvp/edDOwPfB2YLemZmHYh8CfgT5LmAJ8CI6zYTJpGMmXKFKZPn+6G7bQq0kw/3RyYK+kpYFk20cyGFDrJzJ4gRG6pi1NTK2wEZoYkjjjiCF577TW22GKL5ris47QI0hj3ZSVXUQIymQzHHnssI0eO5Nhjj3XDdlodeQfUJLWXNAoYBuwI/MfMJmc/zSWwIWT72I899hirVq0qtxzHKQuFRstvA/oTRsmPAn7TLIoaiQ+eOU6gULO8r5ntCiDpj8BTzSOp4SxfvtwN23EihWruz9uzZvZZM2hpNO3bt2efffZxw3YcCtfcu0laErdFmKG2JG6bmXUqubqUZDIZ3n33XbbZZhuuvvrqcstxnBZBXuM2s4pYgzvbx16wYAEvvvgi7du3L7ckx2kRpHkV1mLJHTxzw3actaQN1tDi8FFxxylMxRr3xRdf7IbtOAWo2Gb55Zdfzle+8hUOO+ywcktxnBZJRdXcmUyG888/n6VLl9KxY0c3bMcpQMUYd7aPff311zN16tRyy3GcFk9FGPeaNWvWGTzzGttxilMRfe6XXnqJFStW+OCZ49SDiqi5P/30Uzdsx6knRZcTaglIeg94o2jG+tGNEod3akJca9NTKTqhuNbeZrZeoMGKMO5SIGl6XesrtURca9NTKTqh4VorolnuOE79ceN2nCqlNRv3TeUWUA9ca9NTKTqhgVpbbZ/bcaqd1lxzO05V48btOFVKqzBuST0lPSppnqS5kkYmjp0n6cWY/uuWqFNSP0lTJT0TF0fcu5w6o6b2kp6KK9LMlXRZTO8q6RFJL8e/XVqw1qskvSDpOUkTJHUus9S8WhPHfyjJJK23vt56mFnVfwirpuwRtzcCXgL6AgOAfwLt4rFNW6jOh4GjYvpgYFILeKYCOsbtWsLyzPsAvwZ+HNN/DFzZgrUeAWwQ069syVrjfk/gIcKErm7FymoVNbeZLTKzmXE7A8wDtgS+A/zKzFbGY++WT2VBnQZkA1JuDLxVHoVrscDSuFsbPwYcS4h5T/x7XPOrW5d8Ws3sYVsb2XcqsFVZBCYo8FwBfgv8KLFfkFZh3Ekk9QF2J/wibg8cKGmapMmS9iqruAQ5OkcBV0laAFwN/KR8ytYiqSYu8vgu8IiZTQN6mNkiCD9WwKZllPg5ebQm+RbwQLMLq4O6tEoaAiw0s2fTltOqjFtSR8J64aPMbAnBK64LoYl2AXBXXHq4rNSh8zvA+WbWEzifsDRy2TGz1WbWj1Dj7S1plzJLykshrZIuAj4DxpRJ3jrUofVLwEXAxfUpp9UYt6RagsGMMbN7YvJ/gXtiU+gpYA1hkn7ZyKNzBJDdHg+UfUAtiZktBiYBg4B3JG0OEP+WtauTS45WJI0AjgZOsdixbSkktB4LfBF4VtLrBKOfKWmzQue3CuOOtfEfgXlmdk3i0ERgYMyzPdCWMnoKFdD5FnBw3B4IvNzc2nKR1D07uiypA3AY8AJwH+HHiPj33rIITJBPq6RBwGhgiJl9UkaJn5NH6ywz29TM+phZH0KltIeZvV2orIoI1tAE7A98HZgd+zIAFwJ/Av4kaQ7wKTCizL/e+XR+G7hW0gbACuDM8shbh82B2yTVECqJu8zs75KmELo3pwNvElaJLTf5tL4CtAMeib2xqWZ2dhl1Qh6tDSnIp586TpXSKprljtMaceN2nCrFjdtxqhQ3bsepUty4HadKaZXGLen46FmzY4q8oyR9oRHXOk3S71Pkez3r6SPpyQZc51JJP2yIxkpG0i2S+sbtCxtwfoc49bje69FHz62xOWmTJK0XzFDSrpJure81GkOrNG5gOPAE8LUUeUcBDTbuhmBm+zXn9SDMZ27uazYFZnaGmT0fd+tt3IQ55feY2er6nCRpJ4L9HCRpw2L5zWw2sJWkXg3Q2CBanXHHedv7A6eTMO44Wf9qSbOjf+95kr4HbAE8KunRmG9p4pwTs7/Gko6JDiizJP1TUo8iOjaR9HDM/weCq1/22NL4d3NJjyn4cc+RdGBMHyRpZqw5/pUotm+sOV6L2rPlTZQ0Q8E/+MzkdST9TNI0YF9Jp0t6KZZxc7bFEWdN3S3p6fjZv477We/5xfSL4zlzJN0UZ+Fla7jfSXoyHts7pu8d02bFvzsUKX+SpP6SfgV0iM9qjKTLta7f/hXJZ5LgFOIsOkmHxFr8rvgcfiXpFAX/6tmStkmcdzJwB8Edd0hOmcPiOS9l/2eRv5GuQmkayu2/2twf4FTgj3H7Sdb6T3+HMKc769/bNf59nYTvLLA0sX0icGvc7sLaSUFnAL+J26cBv69Dx3XAxXH7KwQ3vm7JawA/AC6K2zUEH+/uwALgizk6L433044wP/4DoDYnTwdgDrBJ3DfgpLi9RbzXrgQ3w8ezuoG/AAfE7V6E6bG595Pv+XVN5LkDOCZuTwJujtsHAXPidqdEGYcBdxcpfxLQv47/TR9gZtxuA7yave9EnrbA24n9Q4DFhFli7YCFwGXx2Ejgd4m8LwG9CT7h9yXSJyX+94OBfyaO7Q/8rbm+661l+mmS4cDv4va4uD+T8EW60aJ/r5l9WM9ytwL+quAs0RaYXyT/QcAJ8Vr/kPRRHXmeJkyPrQUmmtkzkg4BHjOz+XXo/IcF3/SVkt4FehDmIX9P0vExT09gO4LxryYYDARnlMnZ8iSNJ7jEQng2fbXWYa6TpI0s+JyTyFPX8xsg6UeErk1XYC6hBgMYG/M+JqmTwpzqjQjTL7cj/PjUFim/TszsdUkfSNo9PodZZvZBTrZuBGNO8rRFl1VJrxJqZoDZhOAeKLgGv2dmb0j6L+F/1MXMsv/DrJPPDMKPTJZ3CT+izUKrMm5JmxAcL3aRZITa0OKXT6Rzgk/maZ/Yvh64xszuiwZ4aT3LWv9g+NIfRKjZ75B0FeHLmO+8lYnt1cAGUcthwL5m9omkSQndK2xtX7OQq2ubeP7yAnnWe36S2gM3EGrWBZIuZd1nlnsfBlwOPGpmxyv4tE/KV34KbiG0nDYj+BHksjxHD6z7DNck9tew1l6GAzsqeGhBaG0MjddLlrGadW2sfbxms9Da+twnArebWW8LHjY9CTXsAYRf6LMVnDOQ1DWekyHUJlnekbSTpDbA8Yn0jQnNOFjrFVWIxwj9PSQdRWjWr4Ok3sC7ZnYzwVtsD2AKcLCkL+bozMfGwEfRsHck+K7XxVOx3C7xGQxNHHsYODehq18d59f1/LKG877CWEfuSo5fjXkPAD42s49Z9zmeVqT8XFbFVk6WCQTXzr0I4YnWIda0NfFHKBXx/z4M+JKt9dI6lmDwxdie0C1qFlqbcQ8n/MOT3E0YHLmF4MX0nKRnYxqEgPAPKA6oEeKC/R34N7AoUc6lwHhJj5PObfQywkjrTEK/7c068hwCPCNpFsHYrjWz9wheYfdEnX8tcp0HCTX4c4RacWpdmcxsIfALQuSXfwLPAx/Hw98D+seBrOeBujyn1nt+FvyRbyY0aScSuhlJPlJ47XcjYYATQgy2X0r6D6Fllbf8OjTcFI+Piff0KfAowbMq32j4w4Qf97QcRIiIsjCR9hih27J5kXMHAP+ox7UahXuFOZ8jqaOZLY214wTgT2aW+2PYVNeaBPzQzKaXovx4jTaE8ZRhZlanD3zsk3/fzL5eKh3xOu2AyYSByc+K5W8KWlvN7RTmUgU/8jmE7srEsqppBAoTW14B/pXPsAHMbBbhVWep3/P3IkSFbRbDBq+5Hadq8ZrbcaoUN27HqVLcuB2nSnHjdpwqxY3bcaqU/w+IEo3+ET0sMwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.dummy import DummyRegressor\n",
    "\n",
    "# Use the last cell for testing\n",
    "is_train = eis_data.seriesIdx != eis_data.seriesIdx.iloc[-1]\n",
    "x_train, x_test, y_train, y_test = x[is_train], x[~is_train], y[is_train], y[~is_train]\n",
    "# Standardize the training data\n",
    "scaler = StandardScaler()\n",
    "x_train_ = scaler.fit_transform(x_train)\n",
    "# Train\n",
    "dummy_regr = DummyRegressor(strategy='mean') # this is the default setting\n",
    "dummy_regr.fit(x_train_, y_train)\n",
    "# Test\n",
    "x_test_ = scaler.transform(x_test)\n",
    "y_test_pred = dummy_regr.predict(x_test_)\n",
    "title = \"Dummy model test \\n\" r\"MAE: %0.2f, R$^2$: %0.2f\" % (mean_absolute_error(y_test, y_test_pred), r2_score(y_test, y_test_pred))\n",
    "plotyy(y_test, y_test_pred, title=title)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1ce3cbfc948>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Capacity vs. cycle plot\n",
    "fig, ax = plt.subplots(1,1)\n",
    "plt.plot(eis_data.cycle[y_test.index], y_test, 'ok')\n",
    "plt.plot(eis_data.cycle[y_test.index], y_test_pred, 'dr')\n",
    "plt.xlabel('Cycle count')\n",
    "plt.ylabel('Discharge capacity (mAh)')\n",
    "plt.legend(('Actual', 'Predicted'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's pretty clear that this model is no longer working well - the dummy model is actually performing better (MAE of 1.3 mAh, vs. 3 mAh for the linear model). But our test set is quite limited. We want to know how well this model performs on **any** unseen cell, not just **one** unseen cell. For this, we can use cross-validation, where we repeat this process, but holding out each cell in turn for testing and training on the other cells. But recall that we need to 'train' the data standardization for each training set, as well. It's not too hard to imagine how this process would work, but it is quite a few lines of code. Thankfully, sklearn automates this process using 'Pipelines'.\n",
    "\n",
    "### Model pipelines\n",
    "\n",
    "We can set up a simple example pipeline using the easy train/test split from earlier (randomized splitting by data point). Below is one way to instantiate a pipeline. You can also use the `make_pipeline()` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.model_selection import cross_val_predict\n",
    "from sklearn.model_selection import KFold\n",
    "\n",
    "# Instatiate the pipeline object\n",
    "pipe = Pipeline(\n",
    "    [\n",
    "        ('scaler', StandardScaler()),\n",
    "        ('regressor', LinearRegression())\n",
    "    ]\n",
    ")\n",
    "# Run cross-validation\n",
    "# Default is 5 randomized splits, e.g., a 80/20 train/test split repeated 5 times, with non-overlapping splits\n",
    "# Here we instantiate the KFold CV-splitter object so that we can define the random seed for replicability\n",
    "y_pred = cross_val_predict(pipe, x, y, cv=KFold(n_splits=5, shuffle=True, random_state=42))\n",
    "title = \"Cross-validation, 5 randomized splits \\n\" r\"MAE: %0.2f, R$^2$: %0.2f\" % (mean_absolute_error(y, y_pred), r2_score(y, y_pred))\n",
    "plotyy(y, y_pred, color=eis_data.seriesIdx, title=title)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that while we are plotting the entire data set, every one of these predictions is a 'test' prediction. Again, with splits randomized by data point and no domain context (knowledge of which cell each data point is from), it looks like we have a very good model. \n",
    "\n",
    "We can test how well the model performns when being split on a cell basis, i.e., our seriesIdx variable, using functionality already built in."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.model_selection import LeaveOneGroupOut\n",
    "\n",
    "# Instatiate the pipeline object\n",
    "pipe = Pipeline(\n",
    "    [\n",
    "        ('scaler', StandardScaler()),\n",
    "        ('regressor', LinearRegression())\n",
    "    ]\n",
    ")\n",
    "# Also make a dummy pipeline\n",
    "dummy_pipe = Pipeline(\n",
    "    [\n",
    "        ('scaler', StandardScaler()),\n",
    "        ('regressor', DummyRegressor())\n",
    "    ]\n",
    ")\n",
    "# Run cross-validation by group, using a leave-on\n",
    "y_pred = cross_val_predict(pipe, x, y, groups=eis_data.seriesIdx, cv=LeaveOneGroupOut())\n",
    "title = \"Cross-validation, leave-one-out by group \\n\" r\"MAE: %0.2f, R$^2$: %0.2f\" % (mean_absolute_error(y, y_pred), r2_score(y, y_pred))\n",
    "plotyy(y, y_pred, color=eis_data.seriesIdx, title=title)\n",
    "# Dummy results\n",
    "y_pred_dummy = cross_val_predict(dummy_pipe, x, y, groups = eis_data.seriesIdx, cv=LeaveOneGroupOut())\n",
    "title = \"Dummy CV, leave-one-out by group \\n\" r\"MAE: %0.2f, R$^2$: %0.2f\" % (mean_absolute_error(y, y_pred_dummy), r2_score(y, y_pred_dummy))\n",
    "plotyy(y, y_pred_dummy, color=eis_data.seriesIdx, title=title)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is dramatically worse. Again, the dummy model, which simply uses the mean of the training set to make predictions, is actually more accurate on average. \n",
    "\n",
    "In summary, so far:\n",
    "- Linear model error when training on the entire data set is extremely low (MAE = 0.36 mAh)\n",
    "- Linear model error during leave-one-out cross-validation by group is ...\n",
    "    - ...several orders of magnitude worse than training (MAE = 4.27 mAh)\n",
    "    - ...worse than using a dummy model (dummy MAE = 3.85 mAh)\n",
    "\n",
    "These three observations are classic signs that a model is overfit to the data. Overfitting results in extremely poor performance on test data. There are many ways to improve our model performance on test data (also called 'regularizing' the model). \n",
    "\n",
    "Two methods we will explore here are:\n",
    "1. choosing a type of estimator (regressor object) that is less prone to overfitting than a linear model, and\n",
    "2. feature engineering\n",
    "\n",
    "### Model regularization: using a different regressor\n",
    "\n",
    "Simply changing the type of regression model used can dramatically impact results. Here, we will compare three options:\n",
    "- Linear regression\n",
    "- Gaussian process regression\n",
    "- XGBoost regression\n",
    "\n",
    "These three models have substantially different approaches, and should result in different final outcomes. Dramatically simplifying these models: linear models use least-squares minimization to optimize a bias and slopes for each feature; GPR models utilize a kernel function to generate a prior and use log-likelihood minimization to optimize the posterior distribution; XGBoost is a tree based model, using randomization to try and prevent overfitting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.gaussian_process import GaussianProcessRegressor\n",
    "from xgboost import XGBRegressor\n",
    "\n",
    "# Always compare to a dummy\n",
    "dummy_pipe = Pipeline(\n",
    "    [\n",
    "        ('scaler', StandardScaler()),\n",
    "        ('regressor', DummyRegressor())\n",
    "    ]\n",
    ")\n",
    "# Linear regression\n",
    "pipe_linear = Pipeline(\n",
    "    [\n",
    "        ('scaler', StandardScaler()),\n",
    "        ('regressor', LinearRegression())\n",
    "    ]\n",
    ")\n",
    "# Gaussian process regression\n",
    "pipe_gpr = Pipeline(\n",
    "    [\n",
    "        ('scaler', StandardScaler()),\n",
    "        ('regressor', GaussianProcessRegressor())\n",
    "    ]\n",
    ")\n",
    "# XGBoost regressor\n",
    "pipe_xgb = Pipeline(\n",
    "    [\n",
    "        ('scaler', StandardScaler()),\n",
    "        ('regressor', XGBRegressor())\n",
    "    ]\n",
    ")\n",
    "\n",
    "# Dummy\n",
    "y_pred_dummy = cross_val_predict(dummy_pipe, x, y, groups = eis_data.seriesIdx, cv=LeaveOneGroupOut())\n",
    "mae_dummy = mean_absolute_error(y, y_pred_dummy)\n",
    "# Linear\n",
    "y_pred_linear = cross_val_predict(pipe_linear, x, y, groups=eis_data.seriesIdx, cv=LeaveOneGroupOut())\n",
    "mae_linear = mean_absolute_error(y, y_pred_linear)\n",
    "# Gaussian process\n",
    "y_pred_gpr = cross_val_predict(pipe_gpr, x, y, groups=eis_data.seriesIdx, cv=LeaveOneGroupOut())\n",
    "mae_gpr = mean_absolute_error(y, y_pred_gpr)\n",
    "# XGBoost\n",
    "y_pred_xgb = cross_val_predict(pipe_xgb, x, y, groups=eis_data.seriesIdx, cv=LeaveOneGroupOut())\n",
    "mae_xgb = mean_absolute_error(y, y_pred_xgb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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VW+jOcD6l/3J5bZL3bru938o4sp/+dpL/3m9xfDbJ4Umu7LfYfqlf5rVJ/irJx/utlVP6Hse1Sb6UZO8khyS5ZuA5Dk2ycbqvfLKSvCPJ7/bTV/br7f8n+fskL+jbd0vyriRfTnJdktf37U9IckWSa5Jcn+Slffuqfgv4/cA1PPp8leXmhcADVXXutoaquqOq/mxwoaq6H9gIHLJD+3eATcBTJl/qVJ0NPC/JqcDzgfcA/xn4YlX9+2HqVXVDVV2w452T7A7sCfxLP39Q/167rv+7chftr0hyQz+ScFV/iPw7geP7Hsc4A2beDIJ5qKpb6dbdvrtYdE/gyn6L4z7gD+l6FMfRvRm2eRbwKrrrLv0R8K/9VtoXgddU1VeBe5Os7pc/CbhgLC9m8dq9qg4HTmV77+t1wL1V9VPATwEnJzkY+C5wXFX9JHAU8J5+Cxm6y5F8qKoOq6o7pvoKpuvH6cLu+0ryY8DzgBt3aN8LOBS4aiLVzUhVPQi8iS4QTu2vZzbMujo+ySbgn4C9gY/37e+lez89G7gI+NNdtL8d+Lmqeg7wS/3zv53tPY6Lx/AyF8wgmL+5Lo2xoweAT/XT1wOf69+Y1wOrBpZbX1X3VdVW4F62v+kGl/sAcFK6q7UeD3x4QdUvfpf0fzeyfR28GHhN/wG9Gvgxui+vAH+c5Drgs3Rbtfv197mjqr40pZoXjSTv67dCv9w3vSDJtcBngDMHztV5Qb/evg58oqq+Pot6J+zngc10G1yPkeTSfqv9koHmi6tqNfBkus/hm/r2n2H7Z+8v6HoZ36/9b4ALkpxMd57UomQQzEOSpwIPA1uAh3j0enz8wPSDtf1EjUeA7wFU1SM8+mS+7w1MPzIwP7jcR+ne0McCG6vqGwt/JYvatnXwMNvXQYA39ltSq6vq4Kr6DF1XfwXw3P7Dezfb/x/un2LNs3Qj8JPbZqrqDcDRdOsF+n0BVfXcweGjvv3ZwE8AvzHQ61wW+tfzIrpe0GlJ9uex6+o44LV0W/6P0n9+Pw787E6eYmcnYlV//18H/hvdsOSmvke26BgEI0qyAjgXeG//JrkdWJ3kcUkOpBveGbuq+i7d2dfnAH8+iedYAj5N92W1B0CSpyXZE3gSsKWqHkxyFHDQLIuckf8HPD7Jbwy0DX2kS1X9PfAnwFvGXdis9MOD59ANCd0JvAt4N92W+xHb9tP1vt+6ej7w1X76b+l2wkO3AfKF79ee5JCqurqq3k531dID6YaJn7iAlzZ2M7/ExBLxQ/1wxB50PYC/AM7qb/sb4Da67uMNDDFOuwAXAb9M171fyn44yV0D82ftdMlH+wDdMNE1/Yd8K/AyuvXy8SQb6HZ43jy2SpeIqqokLwPOTvJmunVzP6N9sZ8L/G6Sg6vqtgmUOW0nA3dW1eX9/PvptvwPp+tZn5Xkf9D1ILftw9vm+CTPp9tYvqu/H8BvAucneRPdOj5pF+3vSrJt+PIK4O+AO4HT+++UP1kM+wm8xMQS0h9V86Sq+r1Z1yJp+bBHsEQkuZTukL8XzroWScuLPQJJapw7iyWpcQaBJDXOIJCkxhkEktQ4g0CSGvdv8x/onRq8gQIAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "regressors = ['Dummy','Linear','GPR','XGBoost']\n",
    "maes = [mae_dummy, mae_linear, mae_gpr, mae_xgb]\n",
    "plt.bar(regressors, maes)\n",
    "plt.ylabel(r'MAE$_{Test}$ (mAh)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Only XGBoost is better than the dummy regressor. Visualize the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "title = \"XGBoost, CV leave-one-out by group \\n\" r\"MAE: %0.2f, R$^2$: %0.2f\" % (mean_absolute_error(y, y_pred_xgb), r2_score(y, y_pred_xgb))\n",
    "plotyy(y, y_pred_xgb, color=eis_data.seriesIdx, title=title)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is quite a lot better than the linear model, the error is ~10% of the average BOL capacity (35 mAh), and all the trends look correct."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model regularization: feature engineering\n",
    "\n",
    "Feature engineering is a broad term with covering many possible approaches for handling features. Generalizing a bit, there are perhaps three overarching types of feature engineering:\n",
    "- Feature generation (few features -> many)\n",
    "    - Ex.: X__i,j = X_i * X_j for all combinations of features i, j\n",
    "- Feature selection (many features -> few)\n",
    "    - Ex.: Use impedance data from only 100 Hz\n",
    "- Feature extraction (many interrelated features -> few independent features)\n",
    "    - Ex.: Fit ECM to impedance data, use ECM parameters as features\n",
    "\n",
    "Here, we will study two of the options: feature selection and feature extraction. \n",
    "\n",
    "First, feature selection. We can simply consider a feature selection algorithm that uses only the values from a single frequency. We can automate the extraction of this frequency by defining a class, and passing it to the Pipeline. This might seem like a fair bit of work for a simple operation, but the benefit here is that this is now fully automated, and can happen at any point in an arbitrarily complex sequence of feature transformation operations. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.base import BaseEstimator, TransformerMixin\n",
    "# Inherent sklearn classes for transformers, this gives us a bunch of methods for free, see sklearn documentation\n",
    "class SingleFrequencySelector(BaseEstimator, TransformerMixin): \n",
    "    def __init__(self, freq, index=0):\n",
    "        self.freq = freq\n",
    "        self.index = index\n",
    "    \n",
    "    def fit(self, X, y=None):\n",
    "        return self\n",
    "    \n",
    "    def transform(self, X, y=None):\n",
    "        X_ = X.copy()\n",
    "        n = self.freq.size\n",
    "        index = [self.index, self.index+n, self.index+n*2, self.index+n*3]\n",
    "        return X_[:,index]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Test just picking the first frequency."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Linear regression, single frequency\n",
    "index_freq = 0\n",
    "pipe_lin_1freq = Pipeline(\n",
    "    [\n",
    "        ('scaler', StandardScaler()),\n",
    "        ('selector', SingleFrequencySelector(freq=freq, index=index_freq)),\n",
    "        ('regressor', LinearRegression())\n",
    "    ]\n",
    ")\n",
    "# Linear\n",
    "y_pred_lin_1freq = cross_val_predict(pipe_lin_1freq, x, y, groups=eis_data.seriesIdx, cv=LeaveOneGroupOut())\n",
    "title = \"Linear (Frequency: %0.2g Hz) \\n\" % (freq[index_freq]) + \"CV leave-one-out by group \\n\" + r\"MAE: %0.2f, R$^2$: %0.2f\" % (mean_absolute_error(y, y_pred_lin_1freq), r2_score(y, y_pred_lin_1freq))\n",
    "plotyy(y, y_pred_lin_1freq, color=eis_data.seriesIdx, title=title)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This wasn't a good frequency to pick - we need to find out if picking a better frequency results in a better model. But we don't want to manually try all 60 frequencies.\n",
    "\n",
    "One of the benefits of automation is that we can take advantage of sklearn functionality to automatically grid-search across all possible frequencies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "# Linear regression, single frequency\n",
    "pipe_lin_1freq = Pipeline(\n",
    "    [\n",
    "        ('scaler', StandardScaler()),\n",
    "        ('selector', SingleFrequencySelector(freq=freq)),\n",
    "        ('regressor', LinearRegression())\n",
    "    ]\n",
    ")\n",
    "# Parameters of any pipeline step can be swept using the name of the step, a dunder '__', and the name of the parameter\n",
    "param_grid = {\n",
    "    \"selector__index\": np.arange(0, freq.size)\n",
    "}\n",
    "search = GridSearchCV(pipe_lin_1freq,\n",
    "    param_grid=param_grid,\n",
    "    cv=LeaveOneGroupOut(),\n",
    "    scoring='neg_mean_absolute_error'\n",
    ")\n",
    "search.fit(x, y, groups=eis_data.seriesIdx)\n",
    "\n",
    "# We can make model predictions using the best result exactly the same as before\n",
    "y_pred_lin_1freq = cross_val_predict(search.best_estimator_, x, y, groups=eis_data.seriesIdx, cv=LeaveOneGroupOut())\n",
    "title = \"Linear (Frequency: %0.2g Hz) \\n\" % (freq[search.best_params_['selector__index']]) + \"CV leave-one-out by group \\n\" + r\"MAE: %0.2f, R$^2$: %0.2f\" % (mean_absolute_error(y, y_pred_lin_1freq), r2_score(y, y_pred_lin_1freq))\n",
    "plotyy(y, y_pred_lin_1freq, color=eis_data.seriesIdx, title=title)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is better than the XGBoost model, which has a much more complicated model architecture. All we had to do was find a good frequency. But we're still making some extreme predictions near end-of-life.\n",
    "\n",
    "We can investigate the MAE versus frequency to help see how the frequency impacts model performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Linear models, single frequency')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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s0I+dMVLn999/D/qxwSZP881XX7kdgTEmQgR7Dh1v3kMzQ/EfhLXwfVG8uLMYYwo0VQ3JkMwMoR6LbwnfF++95yzGmAJt9+7d7N+/P2QJv1KlShQuXNgSvqsmT3YWY0yBFsoTtgBRUVFUrlzZEr4xxrgtI+GHqg8foFmzZiE5IQx20tYYY3y2du1aSpYsSaVKlUJWxmeffRayY1sL3xhjfJScnMxll12GMztM3mMJ3xhjfJSR8PMq69LxxcKFbkdgjHHZ/v372bVrV0j770PNWvjGGOODUI/QCQdL+L54/XVnMcYUWJbwC4pZs5zFGFNgrV69mujoaKpVq+Z2KAGzhG+MMT5ITEykWbNmREVFuR1KwCzhG2NMDvbs2cPq1atp06aN26HkiiV8Y4zJQWJiIkCeT/g2LNMXxYq5HYExxkULFiygVKlSNGjQwO1QcsUSvi++/trtCIwxLkpISODqq6+mcOG8nTKtS8cYY85i8+bNbN68Oc9354AlfN/83/85izGmwElISADyfv89BNilIyKTgJOep7tU9fHghRSBPL9wBg92Nw5jTNglJCRQoUKFPD2lQoZAO6S+V9W3AEQkNBM3G2OMy1SVb775hmuvvTbPzpDpLdAunVtEZICIVFfVVF92EJEaIrLSazkgIgMDLN8YY0IuKSmJPXv25IvuHAi8hd8dqAt0EpGLVbVPTjuo6nqgHoCIRAE7gGkBlm+MMSGXn/rvIfCEfy9wGXAEeCmA/dsAG1U1NDduDLYQ3W7MGBPZEhISuPjii6latarboQRFoAm/jKr+U0SKAm8CD/i5f1dgYoBlh98XX7gdgTEmzE6dOsW3337LnXfe6XYoQRNoH/4JEbkCUKCEPzt6viQ6AP/JYl1fEflJRH5KSUkJMDRjjMm9ZcuWcfDgwXzTnQOBJ/yncbplRgOT/dz3BmC5qu7OvEJVR6tqQ1VtWK5cuQBDC4Enn3QWY0yBkdF/37p1a5cjCR6/u3RE5HkgClgJfKmqv/l5iDvIS905AN9/73YExpgwS0hIoF69esTGxrodStD43cJX1SHA28BB4DYR+dDXfUWkOHAtMNXfco0xJlyOHDnCkiVL8lV3DgR+0vZfqjocmCMil/q6k6oeAWzIizEmoi1evJgTJ04U7IQvIufijMq5VESOAauAPkCv4IdmjDHuSEhIoHDhwlx11VVuhxJUfiV8Vd0P9BKRdkAKUAfI/2MWK1d2OwJjTJioKjNmzKBp06bExMS4HU5QBdql0xRoDOwElgcvnAj16aduR2CMCZNFixaRnJzM2LFj3Q4l6AIdlnku8APwAlAjaNEYY4zLRo0axbnnnkuXLl3cDiXoAk34f+IMzdzjeZy/DRzoLMaYfO2PP/7giy++oFevXhQvXtztcIIukHH4/wImAYeAt4Bfgh1UxFm50u0IjDFhMGbMGE6dOkW/fv3cDiUkAunDTwFGAucAqcD5QY3IGGNckJaWxgcffECbNm2oXr262+GERCAXXs0DlqpqS6AHkL9OYxtjCqTZs2ezbds27r//frdDCZlA+/BLiUgD4Dh+Tp5mjDGRaNSoUVSqVIkOHTq4HUrIBJrwHwGaA+8Dc4MXToSqXt1ZjDH50saNG5kzZw59+/alcOFAR6tHvtxOnvZ1AJOn5T2jR7sdgTEmhD744AOioqLo0yfHm/flaX4nfFUdIiLnA/VxJk/7h6reE/zQjDEm9I4dO8bYsWO55ZZbuOCCC9wOJ6QCaeF/jDMkczkwi4IwLLNvX+entfSNyXf+85//kJqamq9P1mYIpIXfS0SKAVcAbYEHgb7BDiyi/Pqr2xEYY0Lkvffeo3r16lxzzTVuhxJyAZ2dUNWjwGLPYowxedLixYv54YcfeOuttxARt8MJuYASvog8hzN52g5ghaqODGZQxhgTDq+++iply5bl7rvvdjuUsMjt5GkvYpOnGWPyoKSkJGbOnMmAAQMoUaJgXE4U6IDTPz37FozJ0+rVczsCY0yQDRs2jOLFi9O/f3+3QwmbQBP+6zit/LeBpKBFE6lGjHA7AmNMEP3+++9MmDCB+++/n7JlC85dVwNN+O8APwL3qGp6EOMxxpiQe/PNN1FVHnnkEbdDCauA+vBV9W4gGfhQRPLvxBMZunVzFmNMnpeamsro0aO58847iYuLczucsAoo4YtIS6A2cBTnrlf52/btzmKMyfNGjhzJkSNHePzxx90OJewC7dK5HFgNfKaqfwUxHmOMCZnDhw/z9ttvc9NNN1GrVi23wwm7QC+8GhXIfiJyLvARUAtQoLeqfh/IsYwxxl9jx44lNTWVJ554wu1QXBHwPKAiEquqe/3c7S1gjqreLiJFgfx300hjTEQ6duwYb7zxBs2aNaNFixZuh+OK3Ez8PBbw+YStiJQCWgJ3AajqCeBELsoPn6ZN3Y7AGJNLTz75JFu3buXDDz90OxTX5Cbh+zvxxEU498P9WETqAj8DD6nq4dMHFOmLZyK2qlWr5iK0IHv5ZbcjMMbkwty5cxkxYgQPPPAA1157rdvhuCbQqRXA6YP3R2GcGTZHqWp94DBwRkeaqo5W1Yaq2rBcuXK5CM0YYxwpKSncddddXH755QwbNsztcFyVm4Tvbwt/O7BdVX/0PJ+C8wUQ+W67zVmMMXmKqtK7d2/27dvHhAkTKFasmNshuSo3XTpP+rOxqv4hIttEpIaqrgfaAGtzUX74pKa6HYExJgDvv/8+s2bNYsSIEdSpU8ftcFwXcMJX1UDm0BkAfOYZobMJ6BVo+cYYczZr167lkUceoV27dgwYMMDtcCJCoPPhX6Oq34hIRWCPqqb5sp+qrgQaBlKmMcb46vjx49x5552ULFmS+Ph4ChXKTe91/hFoC/96EfkVGAlsxbnNoTHGRIQXX3yRVatWMWPGDCpUqOB2OBEj0IR/LjAIeBzI/7eKadPG7QiMMT5avXo1L7/8Mt26daN9+/ZuhxNRAk34zwOXqup6Ecn/0yMPHux2BMYYH6SlpdGnTx/KlCnDm2++6XY4ESfQhF9dVRd4+vCfDmZAxhgTqLfeeotly5YxceJEYmNj3Q4n4gR6JuN6EakMvA/k/6/RG25wFmNMxNq0aRPPPPMMN998M126dHE7nIiUm5uYZ/ThHw9aNJHq6FFnMcZEJFXlnnvuoXDhwowaNQoRf68LLRhy04dfw9OH79OQTGOMCZWPP/6Yb775hlGjRlG5cmW3w4lYgSb8e4FLRaQ7YDOLGWNcs2vXLv7973/TsmVL+vbt63Y4ES3gLh1V/SfOzJY2Bt8Y44pTp05xxx13cPz4cUaPHm0XWOUg0Bb+CRG5AlgDlAhiPJHp5pvdjsAYk4VBgwbx7bffMn78eGrUqOF2OBEv0IQ/DOiOMzfO58ELJ0I9+qjbERhjMpk0aRLDhw9nwIABdOvWze1w8oRAE34PVX0tqJEYY4yPkpKSuPvuu2nRogWvv/662+HkGYEm/FtE5Agw3zPVcf529dXOz4UL3YzCGAPs37+fjh07UqpUKSZPnkzRokXdDinPCDThdwLqAx1F5GJV7RPEmIwxJkvp6en06NGDLVu2sHDhQipWrOh2SHmK3wlfRF4GBqvqHBGZB5QMfljGGHOmU6dO8dBDDzFz5kzeeecdmjdv7nZIeU4gY5hiVPUUgKqmA8ODG5IxxpwpNTWVdu3a8d577/Hoo4/ywAMPuB1SnhRIl07m2TEPBSMQY4zJypo1a7jlllvYsWMH8fHx9OzZ0+2Q8qxAEv5iEXkdGAGkAeWCGlEk6tzZ7QiMKZCmTp1Kjx49KFWqFIsWLaJx48Zuh5Sn+Z3wVXWyiPwOPAOcA7wQ9Kgizf33ux2BMQWKqvLCCy8wZMgQGjduzNSpU6lUqZLbYeV5Ofbhi8jjXo//CaCqP6hqP2CXqq4NYXyR4cgRZzHGhNzJkyfp06cPQ4YMoXv37ixcuNCSfZD4ctK2q9fjJzOtuz6IsUSuG290FmNMSB08eJD27dszduxYhgwZwrhx44iOjnY7rHzDly4dyeZxVs+NMSYgu3bt4qabbmL16tV89NFH3H13/r9ddrj5kvA1m8dZPTfGGL8lJydzww03sHfvXmbOnMkNdoe5kPAl4dcVkQM4rflinsd4nvv1v5aIbAEO4ozuOaWqDf3Z3xiTvyQnJzNs2DA+/fRTzjvvPL799lsaNGjgdlj5Vo4JX1Wjglxma1XdG+RjGmPykO+//55XX32V6dOnU6xYMfr27cuTTz7JBRdc4HZo+Vqgc+kgIs2BO1U1/1/ydtddbkdgTL6wfv167rvvPhITEylTpgyDBw9mwIABlCuX/y/niQR+JXwRqQfcCXQGNgNT/SxPgXkiosAHqjo60/H74txFi6pVq/p56BCyhG9Mrpw6dYrhw4czZMgQihcvzvDhw7nnnnuIiYlxO7QCJceELyLVcYZm3gGk4tzwRFS1dQDlNVfVnSJSHpgvIutUdVHGSs8XwGiAhg0bRs4J4b2eHqjYWHfjMCYPSkpKonfv3ixbtoyOHTvy3nvvUaFCBbfDKpB8GYe/DmgDtFfVFqr6Ds5JV7+p6k7Pzz3ANKBRIMcJu9tvdxZjjM+OHTvGCy+8wBVXXMHmzZv5/PPP+eKLLyzZu8iXhH8b8AeQKCIfikgbAhh/LyIlRKRkxmPgOiDJ3+MYYyJbWloaH3/8MdWrV2fw4MF06tSJtWvX0rlzZ0Ts0h035ZjwVXWaqnYBLgUWAg8D54vIKBG5zo+yzgf+KyKrgKXAbFWdE0DMxpgIpKpMnz6dOnXq0Lt3bypUqEBCQgKTJk2yk7IRwueTtqp6GPgM+ExEzgNuB54A5vm4/yagbiBBGmMi25o1a+jXrx9LliyhevXqTJkyhU6dOlmLPsL4ctJ2RnarsLnwjSnQ0tPTeeeddxg0aBClS5fmgw8+oHfv3hQuHPCIbxNCvvxWmgLbgInAjxTE+XPuu8/tCIyJOLt27aJXr17MnTuXm2++mbFjx1rXTYTzJeFXAK7FGZZ5JzAbmKiqv4QysIjSpYvbERgTUWbOnEnv3r05dOgQ7733Hv369bPumzzAl6kV0oA5wBwROQcn8S8Ukec9QzTzv23bnJ9VqrgbhzEuSU9PZ8WKFcyZM4evv/6axYsXU7duXSZMmEDNmjXdDs/4yKeONk+ivwkn2V8IvI3/V9nmXd27Oz8XLnQ1DGPCLSEhgfj4eObOnUtKSgoADRo04MUXX+Tf//4355xzjssRGn/4ctJ2HFAL+BoYqqo2dt6YfG7Tpk088sgjTJ8+ndjYWNq1a8f111/PddddR/ny5d0OzwTIlxZ+d+AwUB140KufTgBV1VIhis0YE2aHDh3i5Zdf5vXXX6dIkSK89NJLPPzww3bXqXzClz58X67GNcbkQWlpafz++++sW7eOpKQkRowYwc6dO+nWrRuvvPKKTVecz9hgWWMKkFOnTrFgwQImTpzIihUr+PXXXzl+/Pjp9VdeeSVTpkyhadOmLkZpQsUSvi/+/W+3IzAmV1atWsUnn3zChAkT+OOPPyhTpgwtWrSgXbt21KhRg0svvZQaNWoQGxtrwyvzMUv4vmjf3u0IjPHbunXrmDJlCpMnT2bNmjUUKVKEm266iR49enDjjTfaCJsCyBK+L9avd37WqOFuHCaiqCp79uzh/PPPdzuU05KSkpgyZQpTpkzhl1+cayObN2/OyJEj6dKlC2XLlnU5QuMmOyHri3vvdRZjvIwbN44KFSowevTonDcOoaNHjxIfH0/jxo2pXbs2zz//PLGxsbzzzjts376d//73v9x///2W7I218I0J1JdffgnAvffeS3p6Ov369Qtr+b/++ivvv/8+8fHx7Nu3j0svvZQRI0bQpUsXu8mIyZIlfGMCcPLkSRITE7nrrrtITU3lvvvuIy0tjQceeCBk5a1Zs4YffviBH3/8kR9++IFff/2VwoUL06lTJ+677z5atWplJ1zNWVnCNyYAS5cu5cCBA9x88820b9+ezp07079/f9LT0xkwYEDQytm7dy9PPPEEn332GceOHQOgfPnyNGnShD59+tC9e3drzRufWcLPg1SVnTt32kUxLpo3bx6FChXimmuuoWjRokyePJmuXbvy4IMPkpaWxsCBA3N1/PT0dMaOHcugQYM4cOAAvXv3pnXr1jRp0oS4uDhryZvAqGpELg0aNNCIMX++s0SIp59+WgF96qmn9OTJk26HUyA1adJEGzdufMZrJ06c0Ntuu00BnTdvXsDHXrVqlTZt2lQBveqqq3TNmjW5DdcUIMBPmk1edT2xZ7dEVMKPIOvXr9ciRYpo1apVFdBWrVrpjh073A6rQNm3b58WKlRIBw8e/Ld1x48f1ypVqmjz5s01PT3d72MPGzZMo6KiNDY2VuPj4wM6hinYzpbwbVimL1audBaXqSoPPvggxYoVY+nSpYwbN45ly5ZRv359EhIS3A6vwPjmm29IT0/nuuuu+9u6okWL8vjjj7N48WIWLVrk13GTkpJ44oknuOmmm1i/fj09e/a0rhsTXNl9E7i9RFQLv1UrZ3HZ1KlTFdARI0acfu2XX37RmjVrqojos88+q2lpaS5GWDDce++9WrJkST1x4kSW648cOaLnn3++XnvttT4fMz09Xdu0aaNlypTRvXv3BitUUwBhLfy878iRIzz88MPUqlXrjKF/NWvWZOnSpXTv3p2hQ4cyfPhwF6MsGObPn0/r1q0pUqRIluuLFSvGo48+yvz58/nxxx99Oua0adNISEjg//7v/+wCKRMylvDziFdeeYWtW7cycuRIChc+c3BViRIliI+Pp2PHjjz11FOsjIDup/xq48aNbNq0KcvuHG/9+vXjvPPO48UXX8zxmEePHuXf//43tWvX5l67otuEUFgTvohEicgKEZkVznLzuo0bN/Laa69x55130rJlyyy3ERE+/PBDYmNjufPOOzly5EiYoywY5s2bB8C111571u1iYmIYOHAgM2fOzPEL+I033mDLli289dZbf/syNyaYwt3CfwhIDnOZed5DDz1EkSJFGDZs2Fm3K1u2LOPGjSM5OZnHH388TNEVLPPnzycuLo5LLrkkx20HDBhAqVKleOmll7LdZtu2bbz00kvcdttttG7dOpihGvN32XXuB3sBKgMJwDXArJy2j6iTtosXO4sLZs2apYC+/vrrPu/z8MMPK6CzZs0KYWQFz8mTJ7VUqVJ6zz33+LzPU089pSKia9euzXJ9165dNTo6Wjdv3hykKE1BRySMwwemAA2Aq7NL+EBf4Cfgp6pVq4b0Tckrrr/+eo2Li8t2REhWjh49qrVr19by5cvr7t27QxhdwbJ48WIFdPLkyT7vs2fPHi1evLh27979b+u+/fZbBXTIkCHBDNMUcGdL+GHpMBSRm4E9qvqziFyd3XaqOhoYDdCwYUMNR2w+WbLE+dmsWViL3b9/PwkJCQwcODDbESFZiY6OZsKECTRs2JDevXszc+ZMG88dBPPnz0dEaNOmjc/7lCtXjnvvvZe3336bqlWrsn//fvbs2UNKSgpJSUlUqVKFQYMGhTBqY7xk900QzAV4GdgObAH+AI4An55tn4jq0nFpHP748eMV0O+//z6g/d966y0FdNCgQXbFZhA0a9ZMGzVq5Pd+O3bs0FKlSimgZcqU0Ro1amiLFi20U6dOumTJkhBEagoy3G7hq+qTwJMAnhb+o6raLRxl52VffPEFF1xwAY0aNQpo/wEDBrB27VpeffVV9uzZw+jRo20USID++usvfvzxR5544gm/961UqRK7d+8mKirKr//UjAk2++uPUIcPH2bOnDn06dOHQoUCG0wlIowaNYoKFSowdOhQ9uzZw+TJkylevHiQo83/EhMTSUtLy3H8fXaio6ODHJEx/gv7hVequlBVbw53uXnN119/zbFjx7jttttydRwR4bnnnmPUqFF8/fXXtGnThr179wYpyoJjzpw5xMTE0KRJE7dDMSZgdqVthJo6dSqxsbG0aNEiKMfr168fU6ZMYcWKFbRo0YKNGzcG5bgFQXp6OjNnzqRdu3YULVrU7XCMCZglfF+MGOEsYXL8+HFmzZrFrbfeGtQ+944dOzJ//nx2797N5ZdfzjPPPMOhQ4eCdvz8avny5ezcuZMOHTq4HYoxuWIJ3xf16jlLmCxYsICDBw/SqVOnoB/7qquuYs2aNdx+++28+OKL1KhRg/Hjx5Oenh70svKLGTNmUKhQIW688Ua3QzEmV8QZxRN5GjZsqD/99JPbYTgWLHB+tm0bluJ69+7NF198QUpKSki7EJYsWcJDDz3ETz/9ROPGjRk+fDjNwnytQV5Qr149SpUq5ff89sa5+fr27dtP34/XBE90dDSVK1f+28gvEflZVRtmuVN24zXdXgrqOPyTJ0/qeeedp//617/CUl5aWprGx8drhQoVFNB27drZ2HAvW7duVUCHDRvmdih50qZNmzQlJcWuAwmy9PR0TUlJ0U2bNv1tHTYffmTZt28fXbt25fnnn/9bV8q3337Ln3/+mevROb4qVKgQPXv25LfffuO1117j559/plmzZlx//fV8//33YYkhks2cORPA+u8DdOzYMcqWLWtXegeZiFC2bFm//3OyhB9mW7dupXnz5kyePJlnn32WTp06cfDgwdPrp06dSvHixWnXrl1Y44qJieGxxx5j8+bNvPrqq2ck/mXLloU1lkgyY8YMatSoQfXq1d0OJc+yZB8agbyvlvDDaPny5TRp0oSdO3eSkJDA22+/zaxZs2jatCkbN24kPT2dadOmccMNN7h2cVRMTAyPP/44mzdv5pVXXmHZsmU0atSIW2+9ldWrV7sSk1sOHDhAYmKite5NvmEJP0y++uorWrZsSdGiRVm8eDGtW7dmwIABzJ07l507d9KoUSNeeeUVdu3aFZLROf6KiYlh0KBBbN68meeff57ExETq1q1L165dWbdundvhhcXcuXM5efKkJfx8YNq0aYjIGZ/dLVu2UKtWLQAWLlzIzTeH9npQ7/Jys01uWML3xQcfOEuAPvroIzp06ED16tX54YcfuPzyy0+va9OmDcuWLaNChQo8/fTTFC1aNOQfPH+UKlWKwYMHs3nzZp566ilmzZrFZZddRtu2bZk0aRLHjx93O8SQmTFjBmXLlqVp06Zuh2JyaeLEibRo0YJJkya5HYqrLOH7okYNZwnAxo0b6du3L23btmXRokVUrFjxb9v84x//4Pvvv6dbt27079+fUqVK5TbioMu4P+umTZt4/vnn2bhxI3fccQeVKlXi4YcfJikpye0Qg+rUqVPMnj2bm266iaioKLfDMblw6NAhFi9ezJgxY/xO+PHx8dx66620b9+eatWq8e677zJ8+HDq169PkyZN+PPPPwFYuXIlTZo0oU6dOnTs2JF9+/YB8PPPP1O3bl2aNm3KyJEjTx83LS2Nxx57jCuvvJI6derwQRYNyl9++YVGjRpRr1496tSpw2+//ZaLd8Fhk6f5wjNSg/bt/d511KhRREVFMXbsWGJiYrLdrlSpUowfPz7QCMOmfPnyDB48mKeffpqEhAQ++ugjRo4cyYgRI6hRowa33HILHTp0oEmTJnk6US5evJh9+/ZZd04QDRw4MMf7+/qrXr16jMjhKvgvv/yS66+/nurVq3PeeeexfPlyrrjiCp/LSEpKYsWKFRw7doyLL76YV199lRUrVvDwww/zySefMHDgQHr06ME777xDq1atGDJkCEOHDmXEiBH06tXr9OuPPfbY6WOOGTOG0qVLs2zZMo4fP07z5s257rrrzjgR+/777/PQQw/xr3/9ixMnTpCWlub3+5OZtfB98cYbzuKnI0eOMGbMGDp16kSlSpVCEJh7ChUqxLXXXsvnn3/Ojh07eOedd6hatSrDhw+nRYsWVKxYkd69ezNv3jxOnTrldrh+mzFjBkWLFg14dkwTOSZOnEjXrl0B6Nq1KxMnTvRr/9atW1OyZEnKlStH6dKlae9p+NWuXZstW7bw119/sX//flq1agVAz549WbRo0d9e7969++ljzps3j08++YR69erRuHFjUlNT/9aCb9q0KS+99BKvvvoqW7dupVixYgG/BxmshR9CEydOZP/+/fTv39/tUEKqXLly9O/fn/79+/PXX38xZ84cZsyYwdSpU/n444+pUKECd9xxB926daN+/foRP0xPVZkxYwbXXHMNJUuWdDucfCOnlngopKam8s0335CUlISIkJaWhojw2muv+XyMc8455/TjQoUKnX5eqFChszZmVDXbz7qq8s477/xt+PWWLVtOP77zzjtp3Lgxs2fPpl27dnz00Udcc801PsedFWvhh4iq8u6771K7du2gzXiZF5QuXZouXbrw2Wef8ccff/DFF1/QtGlT3n33XRo0aMDll1/OmDFjgvLvaaisX7+eDRs2WHdOPjBlyhR69OjB1q1b2bJlC9u2baNatWr897//DVoZpUuXpkyZMnz33XcAjB8/nlatWnHuuedSunTp02V99tlnp/dp164do0aN4uTJkwD8+uuvHD58+Izjbtq0iYsuuogHH3yQDh06BGVYtCX8EFmyZAkrV66kf//+Ed+iDZXo6Gg6derE1KlT+eOPP/jggw+IiYmhT58+NGjQgG+++cbtELM0Y8YMgNP/upu8a+LEiXTs2PGM12677TYmTJgQ1HLGjRvHY489Rp06dVi5ciVDhgwB4OOPP+aBBx6gadOmZ3TJ9OnTh5o1a3LFFVdQq1Yt7r333r/9t/D5559Tq1Yt6tWrx7p16+jRo0fuA81uzgW3l7w+l07Xrl21dOnSeujQoZCElFelp6frpEmTNC4uTgHt0KGD/vrrr26HpapObGPHjtWYmBht3Lix2+HkC2vXrnU7hHwtq/cXm0snl8aPdxYf7dq1iylTptCrVy9KlCgRwsDyHhGhS5curFu3jpdeeolvvvmGmjVr0r17dyZPnsz+/ftdiSslJYXbbruN3r17c+WVV/Kf//zHlTiMCSVL+L6oUsVZfPThhx9y6tQp7r///hAGlbdFR0fz5JNPsmHDBu655x6++uorunTpQrly5bj66qsZNmwYycnJaBim7/7qq6+oXbs2s2fP5vXXX2fBggVU8eP3bUxeYfPh++Lzz52fXbrkuOnJkyeJi4ujbt26fP311yEOLP84deoUP/74I7Nnz2b27NmnT1BdfPHFtG/fng4dOtC8efO/zf3tD1Vl7969bNiw4fSyatUqpk+fTu3atfn000+pU6dOsKpkgOTkZC677DK3w8i3snp/zzYfviV8X1x9tfNz4cIcN508eTJdunRh5syZETVFQl6zbds2Zs2axcyZM0lISODEiROce+65XHvttaevPKxTpw5VqlQ5fVL84MGDbN68mU2bNrFlyxZ27NjBzp07z1i8b+lYqFAhqlatSufOnRk6dCjR0dFuVTffsoQfWpbwQ8GPhN+qVSu2bdvGb7/9lqevNI0khw4dYt68ecycOZPExES2bt16el3p0qW58MIL2bFjB3v37j1jv+joaCpVqnR6qVixIhdddBEXX3wxF198MRdeeKHdlDzELOGHlr8J3y68CqJ58+axaNEiXnvtNUv2QRQTE0OnTp1OzyJ64MABkpKSWL16NWvWrGHr1q00adKEatWqcdFFF1GtWjWqVavGeeedV2CHxBqTlbAlfBGJBhYB53jKnaKqz4airPT0dAoVCu/56I0bN9K1a1dq1arFfffdF9ayC5pSpUrRrFkzu/+u8UlUVBS1a9dGVYmKiuLdd98N6LMzYsQI+vbt69q9KoIhnFnxOHCNqtYF6gHXi0iTYBeyZ88eKlWqRK9evfjyyy//dvVaKBw6dIhbb70VEWH69OlnnSTNGBNexYoVY+XKlaxatYqXX36ZJ598MqDjjBgxgiNHjgQ5uvAKWwvfc0FAxhmzIp4l6CcQDh8+TJs2bZg2bRrx8fFER0fTtm3b07M4li9f3v+DTpmS7ar09HR69uzJ2rVrmTt3LhdddFEuojfGhNKBAwcoU6bM6efDhg1j8uTJHD9+nI4dOzJ06FAOHz5M586d2b59O2lpaQwePJjdu3ezc+dOWrduTWxsLImJiS7WInBh7cMXkSjgZ+BiYKSq/phpfV+gL0DVqlUDKqNatWp89tlnnDx5ku+++47p06czffp0Zs2axb333kvLli25/fbb6dixo+8zWMbGZrvqxRdfZOrUqQwfPpy2bdsGFLMxBUbGAAhvnTvD/ffDkSNw441/X3/XXc6ydy/cfvuZ63wYSHH06FHq1avHsWPH2LVr1+kpPebNm8dvv/3G0qVLUVU6dOjAokWLSElJoVKlSsyePRuAv/76i9KlSzN8+HASExOJPUs+iHRh7ehW1TRVrQdUBhqJSK1M60erakNVbViuXLlclVWkSBGuueYa3nrrLTZv3szKlSt55pln2LNnD/3796dy5cq0aNGCUaNGceDAgbMfLD7eWTKZPn06Q4YMoXv37gwcODBX8RpjQiOjS2fdunXMmTOHHj16oKrMmzePefPmUb9+fa644grWrVvHb7/9Ru3atVmwYAGDBg3iu+++o3Tp0m5XIXiym3Mh1AvwLPBodutDOZfOL7/8os8//7zWrl1bAS1RooT27dtXly9fnvUOmebSOXz4sI4bN05jYmK0YcOGeuTIkZDFakxeFglz6ZQoUeKM5+XLl9fdu3frI488ou+//36W+6Smpur48eO1efPmOnToUFVVjYuL05SUlJDH64+InUtHRMqJyLmex8WAtoArd8OuWbMmgwcPZtWqVfz444907tyZ8ePHc8UVV9CoUSPefPNNEhIS2LVr1xmX9q9cuZIHHniASpUq0bNnT6pWrcq0adOCcmMCY0zorVu3jrS0NMqWLUu7du0YO3bs6YvxduzYwZ49e9i5cyfFixenW7duPProoyxfvhyAkiVLcvDgQTfDz7Vw9uFXBMZ5+vELAZNVdVYYy/8bEaFRo0Y0atSI4cOHM378eN5//30eeeSR09uUKVOGhLQ00tLTubJ+fc455xxuv/127rnnHlq2bGnjvI2JcBl9+OD0aIwbN46oqCiuu+46kpOTT9+kPiYmhk8//ZQNGzbw2GOPUahQIYoUKcKoUaMA6Nu3LzfccAMVK1bMsydt7UrbTFSV3bt3s3bt2tPLPRMmoOnpLH7xRbp163bGWX5jTPbsStvQsittc0lEqFChAhUqVPjf7cTWrgXgigEDXIzMGGNyxxK+L776yu0IjDEm1yzh+yIPX0ptjNv0LDfzNoELpDveboDii/fecxZjjF+io6NJTU0Ny41sChJVJTU11e8pva2F74vJk52fdgcrY/xSuXJltm/fTkpKituh5DvR0dFUrlzZr30s4RtjQqZIkSJUq1bN7TCMh3XpGGNMAWEJ3xhjCghL+MYYU0BE7JW2IpICbM1xw+yVBv4KcJusXs/8mvfznB7HAmfecNV3ualHduv8rYv3a27Vxd96ZH6el+sSys/X2eL0ZX0k1cU+X444Vc16uuHsZlXL6wswOtBtsno982vez3N6zFlmrwtlPYJVl0yvuVIXf+uRn+oSys+XL3UJ9ecrWHWxz1fOS37u0pmZi22yej3zazP9fByo3NQju3X+1iUY9fD1OMH6nWR+npfrEsrPly/HCfXny5cYfGGfrxxEbJdOfiIiP2k2kxnlNVaXyJNf6gFWl1DLzy38SDLa7QCCyOoSefJLPcDqElLWwjfGmALCWvjGGFNAWMI3xpgCwhK+McYUEJbwXSYiF4nIGBGZ4nYs/hKREiIyTkQ+FJF/uR1PbuTl30NmInKr53cyXUSuczue3BCRy0TkfRGZIiL3uR1Pbnj+Xn4WkZvdisESfi6IyFgR2SMiSZlev15E1ovIBhF54mzHUNVNqnp3aCP1nZ916gRMUdV7gA5hDzYH/tQl0n4PmflZly89v5O7gC4uhHtWftYlWVX7AZ2BiBriGMDf/yBgcnijPJMl/NyJB673fkFEooCRwA1ATeAOEakpIrVFZFampXz4Q85RPD7WCagMbPNslhbGGH0Vj+91iXTx+F+XZzzrI008ftRFRDoA/wUSwhtmjuLx/e+/LbAW2B3uIL3ZfPi5oKqLROTCTC83Ajao6iYAEZkE3KKqLwOu/SvnK3/qBGzHSforicDGg591WRvm8PziT11EJBl4BfhaVZeHN9Kc+ft7UdUZwAwRmQ1MCGuwZ+FnPWKAEjhfAkdF5CtVTQ9nvBCBf6T5wAX8r9ULTlK8ILuNRaSsiLwP1BeRJ0MdXICyq9NU4DYRGUXwLikPtSzrkkd+D5ll93sZALQFbheRfm4EFoDsfi9Xi8jbIvIB8JU7ofkly3qo6tOqOhDnC+tDN5I9WAs/FLK6W3O2V7epaioQ6X+UWdZJVQ8DvcIdTC5lV5e88HvILLu6vA28He5gcim7uiwEFoY3lFw569+/qsaHL5S/sxZ+8G0Hqng9rwzsdCmWYMlPdbK6RKb8UpeIrocl/OBbBlwiItVEpCjQFZjhcky5lZ/qZHWJTPmlLpFdj0Dna7ZFASYCu4CTON/sd3tevxH4FdgIPO12nAW1TlaXyFzyS13yYj1s8jRjjCkgrEvHGGMKCEv4xhhTQFjCN8aYAsISvjHGFBCW8I0xpoCwhG+MMQWEJXwT8UQkTURWei0Xuh1TsIhIfRH5yPP4LhF5N9P6hSKS7bTAIjJJRC4JdZwmf7C5dExecFRV62W1QkQEEHVpMqogeAp4IRf7jwIeB+4JTjgmP7MWvslzRORCEUkWkfeA5UAVEXlMRJaJyGoRGeq17dOem1EsEJGJIvKo5/XTLWcRiRWRLZ7HUSIyzOtY93pev9qzzxQRWScin3m+bBCRK0VkiYisEpGlIlJSRL4TkXpecSwWkTqZ6lESqKOqq3yocwev/3DWi8hmz6rvgLYiYo03kyP7kJi8oJiIrPQ83gw8DNQAeqnq/eLcxu8SnLnIBWfu9JbAYZy5TOrjfNaXAz/nUNbdwF+qeqWInAMsFpF5nnX1gctxJsNaDDQXkaXA50AXVV0mIqWAo8BHOHecGigi1YFzVHV1prIaAkmZXusiIi28nl8MoJ454QFEZDLwref1dBHZANT1oW6mgLOEb/KCM7p0PH34W1X1B89L13mWFZ7nMThfACWBaap6xLOfL5NYXQfUEZHbPc9Le451Aliqqts9x1oJXAj8BexS1WUAqnrAs/4/wGAReQzojXN3pMwqAimZXvtcVft71XWh90oReRzn/fC+k9UeoBKW8E0OLOGbvOqw12MBXlbVD7w3EJGBZH8vglP8r0szOtOxBqjq3EzHuho47vVSGs7fj2RVhqoeEZH5OHc7yu5+rEczlX1WItIG+CfQMtOqaM+xjDkr68M3+cFcoLeIxACIyAXi3C94EdBRRIp5+svbe+2zBWjgeXx7pmPdJyJFPMeqLiIlzlL2OqCSiFzp2b6kV3/6Rzg3Ilmmqn9msW8yni6bnIhIHPAe0FlVMyf36sAvvhzHFGzWwjd5nqrOE5HLgO8951EPAd1UdbmIfI5zz92tOCc4M7wOTBaR7sA3Xq9/hNNVs9xzUjYFuPUsZZ8QkS7AOyJSDKel3RY4pKo/i8gB4ONs9l0nIqVFpKSqHsyhmncBZYFpnjruVNUbReR8nC6eXTnsb4xNj2wKDhF5DicRvx6m8irh3J7v0uyGjYrIw8BBVf0owDIeBg6o6piAAzUFhnXpGBMCItID+BHnBhhnu0ZgFGeeG/DXfmBcLvY3BYi18I0xpoCwFr4xxhQQlvCNMaaAsIRvjDEFhCV8Y4wpICzhG2NMAWEJ3xhjCoj/B2Bvio9ISFjdAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.semilogx(freq, -1*search.cv_results_['mean_test_score'], '-k')\n",
    "plt.axvline(freq[search.best_params_['selector__index']], color='r', linestyle='--')\n",
    "plt.xlabel('Frequency (Hz)')\n",
    "plt.ylabel(r'MAE$_{Cross-validation}$')\n",
    "plt.legend(['All models', 'Best'])\n",
    "plt.title('Linear models, single frequency')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that we are basically constructing our own feature selection method here, and optimizing the hyperparameters of the feature selection algorithm with an exhaustive grid search. For longer searches, sklearn has other tools such as `RandomizedSearchCV`, `HalvingGridSearchCV`, and `HalvingRandomSearchCV`.\n",
    "\n",
    "To get an idea of how a model is making a prediction, there are many tools we can use to iterrogate a model. Some popular examples include SHAP for estimating contributions of each feature, or partial dependence plots. Note that both of these methods are model agnostic - there are also model specific methods. Here we show partial dependence. Partial dependence shows how the average output value varies when the value of a specific feature is set to a single value across the entire data set and varied across the observed range. We also plot the predicted value from every single row of the data set to show how the results vary independently of the feature being probed by the partial dependence calculation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x216 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.inspection import PartialDependenceDisplay\n",
    "index_best = search.best_params_['selector__index']\n",
    "n = freq.size\n",
    "index_best = [index_best, index_best+n, index_best+n*2, index_best+n*3]\n",
    "fig, ax = plt.subplots(1, 4, figsize=(12,3))\n",
    "PartialDependenceDisplay.from_estimator(search.best_estimator_,\n",
    "    x, features=index_best, kind='both',\n",
    "    ice_lines_kw = {'linestyle': '-', 'color': 'k', 'linewidth': 1},\n",
    "    pd_line_kw = {'linestyle': '--', 'color': 'r', 'linewidth': 2},\n",
    "    ax=ax,\n",
    ")\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From above, the Z_Real and |Z| features seem by far the most important, but note the y-scale: the impact is huge, causing the expected value of the output to be extreme. Also, our two most important features are cancelling each other out - this implies our model is extremely sensitive, because if we get some values of those two features that are not well covered in our training data, the model prediction may be extremely bad. This is likely what is causing the very poor predictions at low capacity.\n",
    "\n",
    "Partial dependence can also be done on two features simultaneously, performing a 2D grid search. We can try this on our two most important features to see what happens."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.set_cmap('cividis')\n",
    "PartialDependenceDisplay.from_estimator(search.best_estimator_,\n",
    "    x, features=[(index_best[0], index_best[2])],\n",
    ")\n",
    "# Scatter of feature values from the actual data\n",
    "feature = x.columns[[index_best[0], index_best[2]]]\n",
    "n_series = np.unique(eis_data.seriesIdx).size\n",
    "cmap = plt.get_cmap('tab10', n_series)\n",
    "sc = plt.scatter(x[feature[0]].values, x[feature[1]].values, c=eis_data.seriesIdx, cmap=cmap)\n",
    "cbar = plt.colorbar(sc)\n",
    "tick_locs = (np.arange(n_series) + 1.5)*(n_series-1)/n_series\n",
    "cbar.set_ticks(tick_locs)\n",
    "cbar.set_ticklabels(np.arange(n_series)+1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Points corresponding to the training data have been added to the partial dependence plot. We can see that these two features are highly correlated, and as noted, they have opposing slopes (resulting in the diagonal trend for the partial dependence). Basically, this model is a see-saw, with extremely heavy weights on either side, which is currently precariously balanced to give accurate predictions in an extremely narrow regime - if the value of either parameter varies even a tiny bit from the distribution of the training data, this model will make an extremely poor prediction.\n",
    "\n",
    "We can compare this partial dependence plot to one calculated using an XGBoost estimator to see how different the model structure is. Let's just use the same frequencies, though XGBoost may have a different set of optimal frequencies compared to a linear model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# XGBoost regression, single frequency\n",
    "index_freq = search.best_params_['selector__index']\n",
    "pipe_xgb_1freq = Pipeline(\n",
    "    [\n",
    "        ('scaler', StandardScaler()),\n",
    "        ('selector', SingleFrequencySelector(freq=freq, index=index_freq)),\n",
    "        ('regressor', XGBRegressor())\n",
    "    ]\n",
    ")\n",
    "# Linear\n",
    "y_pred_xgb_1freq = cross_val_predict(pipe_xgb_1freq, x, y, groups=eis_data.seriesIdx, cv=LeaveOneGroupOut())\n",
    "title = \"XGBoost (Frequency: %0.2g Hz) \\n\" % (freq[index_freq]) + \"CV leave-one-out by group \\n\" + r\"MAE: %0.2f, R$^2$: %0.2f\" % (mean_absolute_error(y, y_pred_xgb_1freq), r2_score(y, y_pred_xgb_1freq))\n",
    "plotyy(y, y_pred_xgb_1freq, color=eis_data.seriesIdx, title=title)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x216 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.inspection import PartialDependenceDisplay\n",
    "index_best = search.best_params_['selector__index']\n",
    "n = freq.size\n",
    "# Need to fit the model on all data first\n",
    "pipe_xgb_1freq.fit(x, y)\n",
    "# Then plot partial dependencies\n",
    "index_best = [index_best, index_best+n, index_best+n*2, index_best+n*3]\n",
    "fig, ax = plt.subplots(1, 4, figsize=(12,3))\n",
    "PartialDependenceDisplay.from_estimator(pipe_xgb_1freq,\n",
    "    x, features=index_best, kind='both',\n",
    "    ice_lines_kw = {'linestyle': '-', 'color': 'k', 'linewidth': 1},\n",
    "    pd_line_kw = {'linestyle': '--', 'color': 'r', 'linewidth': 2},\n",
    "    ax=ax,\n",
    ")\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The relationships are obviously non-linear now, and the expected values (y-axis) have become way more reasonable. The second and fourth features seem like the most important."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.set_cmap('cividis')\n",
    "PartialDependenceDisplay.from_estimator(pipe_xgb_1freq,\n",
    "    x, features=[(index_best[1], index_best[3])],\n",
    ")\n",
    "# Scatter of feature values from the actual data\n",
    "feature = x.columns[[index_best[1], index_best[3]]]\n",
    "n_series = np.unique(eis_data.seriesIdx).size\n",
    "cmap = plt.get_cmap('tab10', n_series)\n",
    "sc = plt.scatter(x[feature[0]].values, x[feature[1]].values, c=eis_data.seriesIdx, cmap=cmap)\n",
    "cbar = plt.colorbar(sc)\n",
    "tick_locs = (np.arange(n_series) + 1.5)*(n_series-1)/n_series\n",
    "cbar.set_ticks(tick_locs)\n",
    "cbar.set_ticklabels(np.arange(n_series)+1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, the relationships are highly non-linear. It seems like there's two separated 'low capacity' regions, which may correspond to cells showing different behaviors in the training set - there seem to be two 'groups' of cells, with data from most cells near the top of the plot, and data from 2 cells near the bottom. But, unlike the linear model, the predictions are much more consistent, not varying wildly outside of the expected range, indicating this model may be more robust to outliers or use in the real-world than the linear model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's unlikely that this is the absolute best we can do. We can try other models (support vector regression, XGBoost, GPR), extracting multiple frequencies instead of 1, or trying to use domain knowledge (such as pulling interesting points of the Nyquist plot). For example, sklearn has built-in feature selection methods, such as `SelectKBest`, which only keeps the *k* features that are most highly scored versus the target variable, using some scoring function."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compare current results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import max_error\n",
    "\n",
    "# x labels\n",
    "regressors = ['Dummy',\n",
    "    'Linear',\"Linear \\n (%0.2g Hz)\" % (freq[search.best_params_['selector__index']]),\n",
    "    'XGBoost', \"XGBoost \\n (%0.2g Hz)\" % (freq[search.best_params_['selector__index']])]\n",
    "# mean absolute errors\n",
    "mae_lin_1freq = mean_absolute_error(y, y_pred_lin_1freq)\n",
    "mae_xgb_1freq = mean_absolute_error(y, y_pred_xgb_1freq)\n",
    "maes = [mae_dummy, mae_linear, mae_lin_1freq, mae_xgb, mae_xgb_1freq]\n",
    "# max absolute errors\n",
    "max_errors = [\n",
    "    max_error(y, y_pred_dummy),\n",
    "    max_error(y, y_pred_linear),\n",
    "    max_error(y, y_pred_lin_1freq),\n",
    "    max_error(y, y_pred_xgb),\n",
    "    max_error(y, y_pred_xgb_1freq),\n",
    "]\n",
    "\n",
    "# Plot\n",
    "fig, ax1 = plt.subplots()\n",
    "\n",
    "b1 = ax1.bar(regressors, maes, color='tab:blue', edgecolor='k', label='MAE', width=-0.3, align='edge')\n",
    "ax1.bar_label(b1, fmt='%.1f')\n",
    "ax1.set_ylabel(r'MAE$_{Test}$ (mAh)', color='tab:blue')\n",
    "\n",
    "ax2 = ax1.twinx()\n",
    "b2 = plt.bar(regressors, max_errors, color='tab:orange', edgecolor='k', label='MaxAE', width=0.3, align='edge')\n",
    "ax2.bar_label(b2, fmt='%.1f')\n",
    "ax2.set_ylabel(r'MaxAE$_{Test}$ (mAh)', color='tab:orange')\n",
    "\n",
    "plt.tight_layout()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Even though the average error of the linear single frequency model is the best, the maximum error is very large, indicating the high sensitivity of the model that we found using partial dependence. The best result is the model with both low average error and low maximum error, in this case, the XGBoost model. Given that choosing a best model often balances multiple metrics and domain considerations (computation time for training or making predictions, memory requirements), it is not always obvious what the 'best model' is."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Summary\n",
    "- Process data into a format for easy input into machine-learning libraries and/or visualization tools\n",
    "- Visualize your target variable(s) and feature(s) as best you can\n",
    "    - Use domain knowledge to help interpret the complexity of your data and the modeling challenge\n",
    "    - Consider physical relationships between variables that could guide you modeling effort\n",
    "    - Be careful of data leakage (spurious correlations, i.e., is your model learning physical relationships or learning how you set up your experiment/processed your data?)\n",
    "    - Outliers can substantially impact modeling results, and are often not obvious\n",
    "- Split you data into training, test, and validation splits that address a real-world relevant challenge\n",
    "    - Random train/test splits are often not challenging or interesting\n",
    "    - If there's not enough data to split into three sets, consider using cross-validation or bootstrap resampling on your training set\n",
    "- Try many possible approaches before assuming any one clever idea is best\n",
    "    - Use automation to optimize hyperparameters of any particular approach\n",
    "- Interrogate models by visualizing results in domain context and using model interrogation methods to try and develop an intuition for model behavior\n",
    "- Use multiple error metrics when deciding on a 'best' model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Challenge\n",
    "Find a model with lower error than the linear, single frequency model above. Some ideas:\n",
    "- Search for the best combination of two frequencies\n",
    "- Use a more complex feature selection strategy \n",
    "    - Ex., using feature importance from a `RandomForestRegressor`, see `SelectFromModel`\n",
    "- Use a different estimator that better handles non-linear relationships, combined with feature selection approaches\n",
    "    - Ex., `XBGRegressor`, `SVR`, `GaussianProcessRegressor`\n",
    "- Extract features using domain knowledge\n",
    "    - Ex., track minima or maxima from Nyquist plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Search for the best combination of two frequencies"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "<summary>Click for code hints for above</summary>\n",
    "\n",
    "```\n",
    "class DoubleFrequencySelector(BaseEstimator, TransformerMixin): \n",
    "    def __init__(self, freq, index_0=0, index_1=0):\n",
    "        self.freq = freq\n",
    "        self.index_0 = index_0\n",
    "        self.index_1 = index_1\n",
    "    \n",
    "    def fit(self, X, y=None):\n",
    "        return self\n",
    "    \n",
    "    def transform(self, X, y=None):\n",
    "        X_ = X.copy()\n",
    "        n = self.freq.size\n",
    "        index_0 = [self.index_0, self.index_0+n, self.index_0+n*2, self.index_0+n*3]\n",
    "        index_1 = [self.index_1, self.index_1+n, self.index_1+n*2, self.index_1+n*3]\n",
    "        index = np.sort(np.concatenate((index_0,index_1)))\n",
    "        return X_[:,index]\n",
    "\n",
    "pipe_lin_2freq = Pipeline(\n",
    "    [\n",
    "        ('scaler', StandardScaler()),\n",
    "        ('selector', DoubleFrequencySelector(freq=freq)),\n",
    "        ('regressor', LinearRegression())\n",
    "    ]\n",
    ")\n",
    "param_grid = {\n",
    "    \"selector__index_0\": np.arange(0, freq.size, step=3),\n",
    "    \"selector__index_1\": np.arange(1, freq.size, step=3), # avoid redundant features\n",
    "}\n",
    "search = GridSearchCV(pipe_lin_2freq,\n",
    "    param_grid=param_grid,\n",
    "    cv=LeaveOneGroupOut(),\n",
    "    scoring='neg_mean_absolute_error'\n",
    ")\n",
    "\n",
    "search.fit(x, y, groups=eis_data.seriesIdx)\n",
    "y_pred_lin_2freq = cross_val_predict(search.best_estimator_, x, y, groups=eis_data.seriesIdx, cv=LeaveOneGroupOut())\n",
    "\n",
    "title = \"Linear (%0.2g Hz, %0.2g Hz) \\n\" % (freq[search.best_params_['selector__index_0']], freq[search.best_params_['selector__index_1']]) + \"CV leave-one-out by group \\n\" + r\"MAE: %0.2f, R$^2$: %0.2f\" % (mean_absolute_error(y, y_pred_lin_2freq), r2_score(y, y_pred_lin_2freq))\n",
    "plotyy(y, y_pred_lin_2freq, color=eis_data.seriesIdx, title=title)\n",
    "```\n",
    "\n",
    "</details>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use a more complex feature selection strategy, ex., SelectFromModel"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "<summary>Click for code hints for above</summary>\n",
    "\n",
    "```\n",
    "from sklearn.ensemble import ExtraTreesRegressor\n",
    "from sklearn.feature_selection import SelectFromModel\n",
    "\n",
    "# Linear regression, select from model\n",
    "pipe_lin_treefeatures = Pipeline(\n",
    "    [\n",
    "        ('scaler', StandardScaler()),\n",
    "        ('selector', SelectFromModel(ExtraTreesRegressor())),\n",
    "        ('regressor', LinearRegression())\n",
    "    ]\n",
    ")\n",
    "y_pred_lin_treefeatures = cross_val_predict(pipe_lin_treefeatures, x, y, groups=eis_data.seriesIdx, cv=LeaveOneGroupOut())\n",
    "title = \"Linear (tree features) \\n CV leave-one-out by group \\n\" + r\"MAE: %0.2f, R$^2$: %0.2f\" % (mean_absolute_error(y, y_pred_lin_treefeatures), r2_score(y, y_pred_lin_treefeatures))\n",
    "plotyy(y, y_pred_lin_treefeatures, color=eis_data.seriesIdx, title=title)\n",
    "```\n",
    "\n",
    "</details>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Try a different estimator with feature selection, ex., SVR with single feature selection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "<summary>Click for code hints for above</summary>\n",
    "\n",
    "```\n",
    "from sklearn.svm import SVR\n",
    "\n",
    "pipe_svr_1freq = Pipeline(\n",
    "    [\n",
    "        ('scaler', StandardScaler()),\n",
    "        ('selector', SingleFrequencySelector(freq=freq)),\n",
    "        ('regressor', SVR())\n",
    "    ]\n",
    ")\n",
    "param_grid = {\n",
    "    \"selector__index\": np.arange(0, freq.size, 3),\n",
    "    \"regressor__C\": np.logspace(-4, 1, 10),\n",
    "}\n",
    "search = GridSearchCV(\n",
    "    pipe_svr_1freq,\n",
    "    param_grid=param_grid,\n",
    "    cv=LeaveOneGroupOut(),\n",
    "    scoring='neg_mean_absolute_error'\n",
    ")\n",
    "search.fit(x, y, groups=eis_data.seriesIdx)\n",
    "\n",
    "y_pred_svr_1freq = cross_val_predict(search.best_estimator_, x, y, groups=eis_data.seriesIdx, cv=LeaveOneGroupOut())\n",
    "title = \"SVR (Frequency: %0.2g Hz) \\n\" % (freq[search.best_params_['selector__index']]) + \"CV leave-one-out by group \\n\" + r\"MAE: %0.2f, R$^2$: %0.2f\" % (mean_absolute_error(y, y_pred_svr_1freq), r2_score(y, y_pred_svr_1freq))\n",
    "plotyy(y, y_pred_svr_1freq, color=eis_data.seriesIdx, title=title)\n",
    "```\n",
    "\n",
    "</details>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extract features using domain knowledge, ex., track the valley in Z_imaginary at low frequency"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "<summary>Click for code hints for above</summary>\n",
    "\n",
    "```\n",
    "class NyquistFeatureSelector(BaseEstimator, TransformerMixin): \n",
    "    def __init__(self, freq):\n",
    "        self.freq = freq\n",
    "    \n",
    "    def fit(self, X, y=None):\n",
    "        return self\n",
    "    \n",
    "    def transform(self, X, y=None):\n",
    "        X_ = X.copy()\n",
    "        n = self.freq.size\n",
    "        zimag1 = X_[:,n:2*n]\n",
    "        zimag2 = zimag1.copy()\n",
    "        # find Zimag valley\n",
    "        maskfreq = self.freq > 10**1\n",
    "        zimag1[:,maskfreq] = np.inf\n",
    "        index_valley = np.argmin(zimag1, axis=1)\n",
    "        # find Zimag peak\n",
    "        maskfreq = self.freq < 10**0\n",
    "        zimag2[:,maskfreq] = np.inf\n",
    "        index_peak = np.argmax(zimag2, axis=1)\n",
    "        # grab other vars and arrange\n",
    "        index = index = np.vstack((\n",
    "            index_valley, index_valley+n, index_valley+2*n, index_valley+3*n,\n",
    "            index_peak, index_peak+n, index_peak+2*n, index_peak+3*n,\n",
    "            )).transpose()\n",
    "        Xnew = np.zeros((X.shape[0],8))\n",
    "        for i in np.arange(index.shape[0]):\n",
    "            Xnew[i,:] = X_[i,index[i,:]]\n",
    "        return Xnew\n",
    "\n",
    "# Linear regression, nyquist features\n",
    "pipe_xgb_nyquistfeatures = Pipeline(\n",
    "    [\n",
    "        ('scaler', StandardScaler()),\n",
    "        ('selector', NyquistFeatureSelector(freq=freq)),\n",
    "        ('regressor', XGBRegressor())\n",
    "    ]\n",
    ")\n",
    "y_pred_xgb_nyquist = cross_val_predict(pipe_xgb_nyquistfeatures, x, y, groups=eis_data.seriesIdx, cv=LeaveOneGroupOut())\n",
    "title = \"XGBoost (Nyquist features) \\n CV leave-one-out by group \\n\" + r\"MAE: %0.2f, R$^2$: %0.2f\" % (mean_absolute_error(y, y_pred_xgb_nyquist), r2_score(y, y_pred_xgb_nyquist))\n",
    "plotyy(y, y_pred_xgb_nyquist, color=eis_data.seriesIdx, title=title)\n",
    "```\n",
    "\n",
    "</details>"
   ]
  }
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